Kaynakça

Ainslie, Joshua, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebrón, and Sumit Sanghai. 2023. “GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints.” Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4895–901.

Akın, Ahmet A. 2019. Zemberek-NLP: Natural Language Processing Tools for Turkish. https://github.com/ahmetaa/zemberek-nlp.

Alammar, Jay. 2019. The Illustrated Word2vec. https://jalammar.github.io/illustrated-word2vec/.

Alayrac, Jean-Baptiste, Jeff Donahue, Pauline Luc, et al. 2022. “Flamingo: A Visual Language Model for Few-Shot Learning.” Advances in Neural Information Processing Systems.

Alt, Christoph, Aleksandra Gabryszak, and Leonhard Hennig. 2020. “TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task.” Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.

Angelov, Dimo. 2020. “Top2Vec: Distributed Representations of Topics.” arXiv Preprint arXiv:2008.09470. https://arxiv.org/abs/2008.09470.

Anthropic. 2024. Building Effective AI Agents. Anthropic Engineering. https://www.anthropic.com/engineering/building-effective-agents.

Anthropic. 2025a. How We Built Our Multi-Agent Research System. Anthropic Engineering. https://www.anthropic.com/engineering/multi-agent-research-system.

Anthropic. 2025b. Introducing Advanced Tool Use on the Claude Developer Platform. Anthropic Engineering. https://www.anthropic.com/engineering/advanced-tool-use.

Arora, Sanjeev, Yingyu Liang, and Tengyu Ma. 2017. “A Simple but Tough-to-Beat Baseline for Sentence Embeddings.” International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=SyK00v5xx.

Aroyo, Lora, and Chris Welty. 2015. “Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation.” AI Magazine 36 (1): 15–24.

Arthur, David, and Sergei Vassilvitskii. 2007. “K-Means++: The Advantages of Careful Seeding.” Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA ’07), 1027–35.

Artstein, Ron, and Massimo Poesio. 2008. “Inter-Coder Agreement for Computational Linguistics.” Computational Linguistics 34 (4): 555–96.

Asai, Akari, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024. “Self-RAG: Learning to Retrieve, Generate, and Critique Through Self-Reflection.” Proceedings of ICLR.

Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016. “Layer Normalization.” arXiv Preprint arXiv:1607.06450. https://arxiv.org/abs/1607.06450.

Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2015. “Neural Machine Translation by Jointly Learning to Align and Translate.” International Conference on Learning Representations.

Bai, Yuntao, Saurav Kadavath, Sandipan Kundu, et al. 2022. “Constitutional AI: Harmlessness from AI Feedback.” arXiv Preprint arXiv:2212.08073. https://arxiv.org/abs/2212.08073.

Barkan, Oren, and Noam Koenigstein. 2016. “Item2Vec: Neural Item Embedding for Collaborative Filtering.” IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). https://arxiv.org/abs/1603.04259.

Barmanbay, Fatih. 2020. Turkish Product Reviews Dataset. Hugging Face Datasets. https://huggingface.co/datasets/fthbrmnby/turkish_product_reviews.

Baroni, Marco, Georgiana Dinu, and Germán Kruszewski. 2014. “Don’t Count, Predict! A Systematic Comparison of Context-Counting Vs. Context-Predicting Semantic Vectors.” Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), 238–47. https://aclanthology.org/P14-1023/.

Bender, Emily M., and Batya Friedman. 2018. “Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science.” Transactions of the Association for Computational Linguistics 6: 587–604.

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT), 610–23. https://doi.org/10.1145/3442188.3445922.

Bengio, Yoshua, Réjean Ducharme, Pascal Vincent, and Christian Janvin. 2003. “A Neural Probabilistic Language Model.” Journal of Machine Learning Research 3: 1137–55. https://www.jmlr.org/papers/v3/bengio03a.html.

Bengio, Yoshua, Patrice Simard, and Paolo Frasconi. 1994. “Learning Long-Term Dependencies with Gradient Descent Is Difficult.” IEEE Transactions on Neural Networks 5 (2): 157–66.

Beurer-Kellner, Luca, Beat Buesser, Ana-Maria Creţu, et al. 2025. “Design Patterns for Securing LLM Agents Against Prompt Injections.” arXiv Preprint arXiv:2506.08837.

Beyer, Betsy, Chris Jones, Jennifer Petoff, and Niall Richard Murphy, eds. 2016. Site Reliability Engineering: How Google Runs Production Systems. O’Reilly Media.

Beyer, Lucas, Olivier J. Hénaff, Alexander Kolesnikov, Xiaohua Zhai, and Aäron van den Oord. 2020. “Are We Done with ImageNet?” arXiv Preprint arXiv:2006.07159.

Blei, David M., Andrew Y. Ng, and Michael I. Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning Research 3: 993–1022. https://www.jmlr.org/papers/v3/blei03a.html.

Bogdanov, Sergei, Alexandre Constantin, Timothée Bernard, Benoit Crabbé, and Etienne Bernard. 2024. “NuNER: Entity Recognition Encoder Pre-Training via LLM-Annotated Data.” Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.

Bojanowski, Piotr, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017. “Enriching Word Vectors with Subword Information.” Transactions of the Association for Computational Linguistics 5: 135–46. https://arxiv.org/abs/1607.04606.

Bolukbasi, Tolga, Kai-Wei Chang, James Zou, Venkatesh Saligrama, and Adam Kalai. 2016. “Man Is to Computer Programmer as Woman Is to Homemaker? Debiasing Word Embeddings.” Advances in Neural Information Processing Systems (NeurIPS) 29. https://arxiv.org/abs/1607.06520.

Bostrom, Kaj, and Greg Durrett. 2020. “Byte Pair Encoding Is Suboptimal for Language Model Pretraining.” Findings of the Association for Computational Linguistics: EMNLP 2020, 4617–24. https://aclanthology.org/2020.findings-emnlp.414/.

Bouma, Gerlof. 2009. “Normalized (Pointwise) Mutual Information in Collocation Extraction.” From Form to Meaning: Processing Texts Automatically, Proceedings of the Biennial GSCL Conference 2009, 31–40.

Bowman, Samuel R., and George E. Dahl. 2021. “What Will It Take to Fix Benchmarking in Natural Language Understanding?” Proceedings of NAACL-HLT.

Bradley, Ralph Allan, and Milton E. Terry. 1952. “Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons.” Biometrika 39 (3/4): 324–45.

Brants, Thorsten, Ashok C. Popat, Peng Xu, Franz J. Och, and Jeffrey Dean. 2007. “Large Language Models in Machine Translation.” Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 858–67.

Broder, Andrei Z. 1997. “On the Resemblance and Containment of Documents.” Proceedings. Compression and Complexity of SEQUENCES 1997, 21–29. https://doi.org/10.1109/SEQUEN.1997.666900.

Brown, Peter F., Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993. “The Mathematics of Statistical Machine Translation: Parameter Estimation.” Computational Linguistics 19 (2): 263–311.

Brown, Peter F., Vincent J. Della Pietra, Peter V. deSouza, Jennifer C. Lai, and Robert L. Mercer. 1992. “Class-Based n-Gram Models of Natural Language.” Computational Linguistics 18 (4): 467–79.

Brown, Tom B. et al. 2020. “Language Models Are Few-Shot Learners.” Advances in Neural Information Processing Systems 33.

Bruch, Sebastian, Siyu Gai, and Amir Ingber. 2024. “An Analysis of Fusion Functions for Hybrid Retrieval.” ACM Transactions on Information Systems 42 (1): 1–35. https://doi.org/10.1145/3596512.

Bucher, Martin Juan José, and Marco Martini. 2024. Fine-Tuned “Small” LLMs (Still) Significantly Outperform Zero-Shot Generative AI Models in Text Classification. https://arxiv.org/abs/2406.08660.

Caliskan, Aylin, Joanna J. Bryson, and Arvind Narayanan. 2017. “Semantics Derived Automatically from Language Corpora Contain Human-Like Biases.” Science 356 (6334): 183–86. https://doi.org/10.1126/science.aal4230.

Campello, Ricardo J. G. B., Davoud Moulavi, and Jörg Sander. 2013. “Density-Based Clustering Based on Hierarchical Density Estimates.” Advances in Knowledge Discovery and Data Mining (PAKDD 2013), Lecture notes in computer science, vol. 7819: 160–72. https://doi.org/10.1007/978-3-642-37456-2_14.

Carlini, Nicholas, Florian Tramer, Eric Wallace, et al. 2021. “Extracting Training Data from Large Language Models.” 30th USENIX Security Symposium, 2633–50.

Cemri, Mert, Melissa Z. Pan, Shuyi Yang, et al. 2025. Why Do Multi-Agent LLM Systems Fail? https://arxiv.org/abs/2503.13657.

Chang, Jonathan, Sean Gerrish, Chong Wang, Jordan L. Boyd-Graber, and David M. Blei. 2009. “Reading Tea Leaves: How Humans Interpret Topic Models.” Advances in Neural Information Processing Systems 22 (NIPS 2009), 288–96.

Charikar, Moses S. 2002. “Similarity Estimation Techniques from Rounding Algorithms.” Proceedings of the Thirty-Fourth Annual ACM Symposium on Theory of Computing (STOC ’02), 380–88. https://doi.org/10.1145/509907.509965.

Chawla, Nitesh V., Kevin W. Bowyer, Lawrence O. Hall, and W. Philip Kegelmeyer. 2002. “SMOTE: Synthetic Minority over-Sampling Technique.” Journal of Artificial Intelligence Research 16: 321–57. https://doi.org/10.1613/jair.953.

Chelba, Ciprian, Tomas Mikolov, Mike Schuster, et al. 2014. “One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling.” Interspeech, 2635–39.

Chen, Lingjiao, Matei Zaharia, and James Zou. 2023. “FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.” arXiv Preprint arXiv:2305.05176.

Chen, Shouyuan, Sherman Wong, Liangjian Chen, and Yuandong Tian. 2023. “Extending Context Window of Large Language Models via Positional Interpolation.” arXiv Preprint arXiv:2306.15595. https://arxiv.org/abs/2306.15595.

Chen, Sizhe, Julien Piet, Chawin Sitawarin, and David Wagner. 2025. “StruQ: Defending Against Prompt Injection with Structured Queries.” 34th USENIX Security Symposium.

Chen, Sizhe, Arman Zharmagambetov, Saeed Mahloujifar, Kamalika Chaudhuri, David Wagner, and Chuan Guo. 2025. “SecAlign: Defending Against Prompt Injection with Preference Optimization.” Proceedings of the 2025 ACM SIGSAC Conference on Computer and Communications Security (CCS). https://doi.org/10.1145/3719027.3744836.

Chen, Stanley F., and Joshua Goodman. 1999. “An Empirical Study of Smoothing Techniques for Language Modeling.” Computer Speech and Language 13 (4): 359–94.

Chen, Yanda, Joe Benton, Ansh Radhakrishnan, et al. 2025. “Reasoning Models Don’t Always Say What They Think.” arXiv Preprint arXiv:2505.05410. https://arxiv.org/abs/2505.05410.

Chhikara, Prateek, Dev Khant, Saket Aryan, Taranjeet Singh, and Deshraj Yadav. 2025. “Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory.” European Conference on Artificial Intelligence (ECAI). https://arxiv.org/abs/2504.19413.

Chinchor, Nancy, and Beth Sundheim. 1993. “MUC-5 Evaluation Metrics.” Proceedings of the Fifth Message Understanding Conference (MUC-5).

Christiano, Paul F., Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017. “Deep Reinforcement Learning from Human Preferences.” Advances in Neural Information Processing Systems 30. https://arxiv.org/abs/1706.03741.

Cicchetti, Domenic V., and Alvan R. Feinstein. 1990. “High Agreement but Low Kappa: II. Resolving the Paradoxes.” Journal of Clinical Epidemiology 43 (6): 551–58.

Clark, Kevin, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019. “What Does BERT Look at? An Analysis of BERT’s Attention.” Proceedings of the 2019 ACL Workshop BlackboxNLP, 276–86.

Clauset, Aaron, Cosma Rohilla Shalizi, and M. E. J. Newman. 2009. “Power-Law Distributions in Empirical Data.” SIAM Review 51 (4): 661–703. https://doi.org/10.1137/070710111.

Cloudflare. 2025. Code Mode: The Better Way to Use MCP. The Cloudflare Blog. https://blog.cloudflare.com/code-mode/.

Cobbe, Karl, Vineet Kosaraju, Mohammad Bavarian, et al. 2021. “Training Verifiers to Solve Math Word Problems.” arXiv Preprint arXiv:2110.14168. https://arxiv.org/abs/2110.14168.

Cohen, Jacob. 1960. “A Coefficient of Agreement for Nominal Scales.” Educational and Psychological Measurement 20 (1): 37–46.

Conneau, Alexis, Kartikay Khandelwal, Naman Goyal, et al. 2020. “Unsupervised Cross-Lingual Representation Learning at Scale.” Proceedings of ACL.

Cormack, Gordon V., Charles L. A. Clarke, and Stefan Buettcher. 2009. “Reciprocal Rank Fusion Outperforms Condorcet and Individual Rank Learning Methods.” Proceedings of SIGIR.

Costa, Manuel, Boris Köpf, Aashish Kolluri, et al. 2025. “Securing AI Agents with Information-Flow Control.” arXiv Preprint arXiv:2505.23643.

Covington, Paul, Jay Adams, and Emre Sargin. 2016. “Deep Neural Networks for YouTube Recommendations.” Proceedings of the 10th ACM Conference on Recommender Systems (RecSys), 191–98. https://doi.org/10.1145/2959100.2959190.

Dao, Tri, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. 2022. “FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness.” Advances in Neural Information Processing Systems (NeurIPS) 35.

Davis, Jesse, and Mark Goadrich. 2006. “The Relationship Between Precision-Recall and ROC Curves.” Proceedings of the 23rd International Conference on Machine Learning (ICML ’06) (Pittsburgh, Pennsylvania, USA), 233–40. https://doi.org/10.1145/1143844.1143874.

Dawid, A. Philip, and Allan M. Skene. 1979. “Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm.” Journal of the Royal Statistical Society: Series C 28 (1): 20–28.

Debenedetti, Edoardo, Ilia Shumailov, Tianqi Fan, et al. 2025. “Defeating Prompt Injections by Design.” arXiv Preprint arXiv:2503.18813.

Debenedetti, Edoardo, Jie Zhang, Mislav Balunović, Luca Beurer-Kellner, Marc Fischer, and Florian Tramèr. 2024. “AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents.” Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track.

Deerwester, Scott, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, and Richard Harshman. 1990. “Indexing by Latent Semantic Analysis.” Journal of the American Society for Information Science 41 (6): 391–407. https://doi.org/10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9.

Dettmers, Tim, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023. “QLoRA: Efficient Finetuning of Quantized LLMs.” Advances in Neural Information Processing Systems 36. https://arxiv.org/abs/2305.14314.

Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” Proceedings of NAACL-HLT.

Dodge, Jesse, Maarten Sap, Ana Marasović, et al. 2021. “Documenting Large Webtext Corpora: A Case Study on the Colossal Clean Crawled Corpus.” Proceedings of EMNLP.

Doogan, Caitlin, and Wray Buntine. 2021. “Topic Model or Topic Twaddle? Re-Evaluating Semantic Interpretability Measures.” Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics, 3824–48.

Dosovitskiy, Alexey, Lucas Beyer, Alexander Kolesnikov, et al. 2021. “An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale.” Proceedings of ICLR.

Dror, Rotem, Gili Baumer, Segev Shlomov, and Roi Reichart. 2018. “The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing.” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1383–92. https://aclanthology.org/P18-1128/.

Edwards, Aleksandra, and Jose Camacho-Collados. 2024. “Language Models for Text Classification: Is in-Context Learning Enough?” Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024) (Torino, Italia), 10058–72. https://aclanthology.org/2024.lrec-main.879/.

Elangovan, Aparna, Jiayuan He, and Karin Verspoor. 2021. “Memorization Vs. Generalization: Quantifying Data Leakage in NLP Performance Evaluation.” Proceedings of EACL.

Elkan, Charles. 2001. “The Foundations of Cost-Sensitive Learning.” Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI’01) (Seattle, Washington, USA) 2: 973–78.

Elman, Jeffrey L. 1990. “Finding Structure in Time.” Cognitive Science 14 (2): 179–211.

Es, Shahul, Jithin James, Luis Espinosa-Anke, and Steven Schockaert. 2024. “RAGAs: Automated Evaluation of Retrieval Augmented Generation.” Proceedings of EACL: System Demonstrations.

Ethayarajh, Kawin. 2019. “How Contextual Are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings.” Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 55–65. https://arxiv.org/abs/1909.00512.

Fabbri, Alexander R., Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021. “SummEval: Re-Evaluating Summarization Evaluation.” Transactions of the Association for Computational Linguistics 9: 391–409.

Fedus, William, Barret Zoph, and Noam Shazeer. 2022. “Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity.” Journal of Machine Learning Research 23 (120): 1–39.

Feinstein, Alvan R., and Domenic V. Cicchetti. 1990. “High Agreement but Low Kappa: I. The Problems of Two Paradoxes.” Journal of Clinical Epidemiology 43 (6): 543–49.

Finkelstein, Lev, Evgeniy Gabrilovich, Yossi Matias, et al. 2002. “Placing Search in Context: The Concept Revisited.” ACM Transactions on Information Systems 20 (1): 116–31. https://doi.org/10.1145/503104.503110.

Firth, John R. 1957. “A Synopsis of Linguistic Theory, 1930–1955.” In Studies in Linguistic Analysis. Blackwell.

Fleiss, Joseph L. 1971. “Measuring Nominal Scale Agreement Among Many Raters.” Psychological Bulletin 76 (5): 378–82.

Frantar, Elias, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh. 2023. “GPTQ: Accurate Post-Training Quantization for Generative Pre-Trained Transformers.” International Conference on Learning Representations (ICLR).

Freitag, Markus, George Foster, David Grangier, Viresh Ratnakar, Qijun Tan, and Wolfgang Macherey. 2021. “Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation.” Transactions of the Association for Computational Linguistics 9: 1460–74.

Frénay, Benoît, and Michel Verleysen. 2014. “Classification in the Presence of Label Noise: A Survey.” IEEE Transactions on Neural Networks and Learning Systems 25 (5): 845–69.

Gage, Philip. 1994. “A New Algorithm for Data Compression.” The C Users Journal 12 (2): 23–38.

Gao, Leo, Stella Biderman, Sid Black, et al. 2020. “The Pile: An 800GB Dataset of Diverse Text for Language Modeling.” arXiv Preprint arXiv:2101.00027. https://arxiv.org/abs/2101.00027.

Gao, Yunfan, Yun Xiong, Xinyu Gao, et al. 2023. “Retrieval-Augmented Generation for Large Language Models: A Survey.” arXiv Preprint arXiv:2312.10997.

Gebru, Timnit, Jamie Morgenstern, Briana Vecchione, et al. 2021. “Datasheets for Datasets.” Communications of the ACM 64 (12): 86–92.

Gema, Aryo Pradipta, Alexander Hägele, Runjin Chen, et al. 2025. “Inverse Scaling in Test-Time Compute.” Transactions on Machine Learning Research (TMLR). https://arxiv.org/abs/2507.14417.

Gemma Team, Google DeepMind. 2025. Gemma 3 Technical Report. Google DeepMind. https://arxiv.org/abs/2503.19786.

Geng, Saibo, Martin Josifoski, Maxime Peyrard, and Robert West. 2023. “Grammar-Constrained Decoding for Structured NLP Tasks Without Finetuning.” Proceedings of EMNLP.

Gerstgrasser, Matthias, Rylan Schaeffer, Apratim Dey, Rafael Rafailov, et al. 2024. Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data. https://arxiv.org/abs/2404.01413.

Geva, Mor, Yoav Goldberg, and Jonathan Berant. 2019. “Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets.” Proceedings of EMNLP-IJCNLP.

Geva, Mor, Roei Schuster, Jonathan Berant, and Omer Levy. 2021. “Transformer Feed-Forward Layers Are Key-Value Memories.” Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 5484–95.

Gilardi, Fabrizio, Meysam Alizadeh, and Maël Kubli. 2023. “ChatGPT Outperforms Crowd Workers for Text-Annotation Tasks.” Proceedings of the National Academy of Sciences 120 (30): e2305016120. https://doi.org/10.1073/pnas.2305016120.

GitHub Advisory Database. 2025. CVE-2025-6514: mcp-remote Exposed to OS Command Injection via Untrusted MCP Server Connections. GHSA-6xpm-ggf7-wc3p. https://github.com/advisories/GHSA-6xpm-ggf7-wc3p.

Goldberg, Yoav, and Omer Levy. 2014. Word2vec Explained: Deriving Mikolov Et Al.’s Negative-Sampling Word-Embedding Method. https://arxiv.org/abs/1402.3722.

Gonen, Hila, and Yoav Goldberg. 2019. “Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings but Do Not Remove Them.” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 609–14. https://arxiv.org/abs/1903.03862.

Google Developers. 2022. Machine Learning Guides: Text Classification. https://developers.google.com/machine-learning/guides/text-classification/.

Goyal, Tanya, Junyi Jessy Li, and Greg Durrett. 2022. “News Summarization and Evaluation in the Era of GPT-3.” arXiv Preprint arXiv:2209.12356.

Grattafiori, Aaron, Abhimanyu Dubey, Abhinav Jauhri, et al. 2024. “The Llama 3 Herd of Models.” arXiv Preprint arXiv:2407.21783. https://arxiv.org/abs/2407.21783.

Grave, Edouard, Piotr Bojanowski, Prakhar Gupta, Armand Joulin, and Tomas Mikolov. 2018. “Learning Word Vectors for 157 Languages.” Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC). https://arxiv.org/abs/1802.06893.

Gray, Mary L., and Siddharth Suri. 2019. Ghost Work: How to Stop Silicon Valley from Building a New Global Underclass. Houghton Mifflin Harcourt.

Grbovic, Mihajlo, and Haibin Cheng. 2018. “Real-Time Personalization Using Embeddings for Search Ranking at Airbnb.” Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 311–20. https://doi.org/10.1145/3219819.3219885.

Greshake, Kai, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz. 2023. “Not What You’ve Signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection.” Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security (AISec). https://doi.org/10.1145/3605764.3623985.

Groeneveld, Dirk, Iz Beltagy, Pete Walsh, et al. 2024. “OLMo: Accelerating the Science of Language Models.” Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 15789–809. https://arxiv.org/abs/2402.00838.

Grootendorst, Maarten. 2022. BERTopic: Neural Topic Modeling with a Class-Based TF-IDF Procedure. https://arxiv.org/abs/2203.05794.

Grusky, Max, Mor Naaman, and Yoav Artzi. 2018. “Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies.” Proceedings of NAACL-HLT.

Guo, Chuan, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017. “On Calibration of Modern Neural Networks.” Proceedings of the 34th International Conference on Machine Learning, Proceedings of machine learning research, vol. 70: 1321–30. https://proceedings.mlr.press/v70/guo17a.html.

Guo, Daya, Dejian Yang, Haowei Zhang, et al. 2025. “DeepSeek-R1 Incentivizes Reasoning in LLMs Through Reinforcement Learning.” Nature 645 (8081): 633–38. https://doi.org/10.1038/s41586-025-09422-z.

Gururangan, Suchin, Ana Marasović, Swabha Swayamdipta, et al. 2020. “Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks.” Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 8342–60. https://arxiv.org/abs/2004.10964.

Gururangan, Suchin, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R. Bowman, and Noah A. Smith. 2018. “Annotation Artifacts in Natural Language Inference Data.” Proceedings of NAACL-HLT.

Gwet, Kilem Li. 2008. “Computing Inter-Rater Reliability and Its Variance in the Presence of High Agreement.” British Journal of Mathematical and Statistical Psychology 61 (1): 29–48.

Harris, Zellig S. 1954. “Distributional Structure.” Word 10 (2–3): 146–62. https://doi.org/10.1080/00437956.1954.11659520.

Hasan, Tahmid, Abhik Bhattacharjee, Md. Saiful Islam, et al. 2021. “XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages.” Findings of ACL-IJCNLP.

He, Horace, and Thinking Machines Lab. 2025. Defeating Nondeterminism in LLM Inference. Thinking Machines Lab blog.

He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. “Deep Residual Learning for Image Recognition.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–78.

Heafield, Kenneth. 2011. “KenLM: Faster and Smaller Language Model Queries.” Proceedings of the Sixth Workshop on Statistical Machine Translation, 187–97.

Heaps, Harold Stanley. 1978. Information Retrieval: Computational and Theoretical Aspects. Academic Press.

Hendrycks, Dan, and Kevin Gimpel. 2016. Gaussian Error Linear Units (GELUs). https://arxiv.org/abs/1606.08415.

Hill, Felix, Roi Reichart, and Anna Korhonen. 2015. “SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation.” Computational Linguistics 41 (4): 665–95. https://arxiv.org/abs/1408.3456.

Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. 2015. “Distilling the Knowledge in a Neural Network.” arXiv Preprint arXiv:1503.02531. https://arxiv.org/abs/1503.02531.

Hochreiter, Sepp, and Jürgen Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–80.

Hoffart, Johannes, Mohamed Amir Yosef, Ilaria Bordino, et al. 2011. “Robust Disambiguation of Named Entities in Text.” Proceedings of EMNLP.

Hoffmann, Jordan, Sebastian Borgeaud, Arthur Mensch, et al. 2022. “Training Compute-Optimal Large Language Models.” Advances in Neural Information Processing Systems 35. https://arxiv.org/abs/2203.15556.

Holtzman, Ari, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020. “The Curious Case of Neural Text Degeneration.” International Conference on Learning Representations.

Hong, Kelly, Anton Troynikov, and Jeff Huber. 2025. Context Rot: How Increasing Input Tokens Impacts LLM Performance. Chroma. https://research.trychroma.com/context-rot.

Houlsby, Neil, Andrei Giurgiu, Stanislaw Jastrzebski, et al. 2019. “Parameter-Efficient Transfer Learning for NLP.” Proceedings of the 36th International Conference on Machine Learning (ICML), 2790–99. https://arxiv.org/abs/1902.00751.

Howard, Jeremy, and Sebastian Ruder. 2018. “Universal Language Model Fine-Tuning for Text Classification.” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 328–39.

Hoyle, Alexander, Pranav Goel, Andrew Hian-Cheong, Denis Peskov, Jordan Boyd-Graber, and Philip Resnik. 2021. “Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence.” Advances in Neural Information Processing Systems.

Hsieh, Cheng-Ping, Simeng Sun, Samuel Kriman, et al. 2024. “RULER: What’s the Real Context Size of Your Long-Context Language Models?” Proceedings of COLM.

Hsieh, Cheng-Yu, Chun-Liang Li, Chih-Kuan Yeh, et al. 2023. “Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes.” Findings of the Association for Computational Linguistics: ACL 2023, 8003–17.

Hu, Edward J., Yelong Shen, Phillip Wallis, et al. 2022. “LoRA: Low-Rank Adaptation of Large Language Models.” International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2106.09685.

Huang, Jie, Xinyun Chen, Swaroop Mishra, et al. 2024. “Large Language Models Cannot Self-Correct Reasoning Yet.” Proceedings of ICLR.

Hubert, Lawrence, and Phipps Arabie. 1985. “Comparing Partitions.” Journal of Classification 2 (1): 193–218. https://doi.org/10.1007/BF01908075.

Hutchins, W. John. 2004. “The Georgetown-IBM Experiment Demonstrated in January 1954.” Machine Translation: From Real Users to Research (AMTA 2004), Lecture notes in computer science, vol. 3265: 102–14. https://doi.org/10.1007/978-3-540-30194-3_12.

Huyen, Chip. 2021. Introduction to Machine Learning Interviews Book. https://huyenchip.com/ml-interviews-book/.

Indyk, Piotr, and Rajeev Motwani. 1998. “Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality.” Proceedings of the 30th Annual ACM Symposium on Theory of Computing, 604–13. https://doi.org/10.1145/276698.276876.

Jelinek, Frederick, and Robert L. Mercer. 1980. “Interpolated Estimation of Markov Source Parameters from Sparse Data.” Proceedings of the Workshop on Pattern Recognition in Practice, 381–97.

Jiang, Albert Q., Alexandre Sablayrolles, Antoine Roux, et al. 2024. Mixtral of Experts. https://arxiv.org/abs/2401.04088.

Joachims, Thorsten. 1998. “Text Categorization with Support Vector Machines: Learning with Many Relevant Features.” Proceedings of the 10th European Conference on Machine Learning (ECML), 137–42. https://doi.org/10.1007/BFb0026683.

Johari, Ramesh, Pete Koomen, Leonid Pekelis, and David Walsh. 2022. “Always Valid Inference: Continuous Monitoring of A/B Tests.” Operations Research 70 (3): 1806–21. https://doi.org/10.1287/opre.2021.2135.

Johnson, Jeff, Matthijs Douze, and Hervé Jégou. 2019. “Billion-Scale Similarity Search with GPUs.” IEEE Transactions on Big Data.

Joulin, Armand, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017. “Bag of Tricks for Efficient Text Classification.” Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 427–31. https://arxiv.org/abs/1607.01759.

Jurafsky, Daniel, and James H. Martin. 2026. Speech and Language Processing. 3rd (draft, August 19, 2026). https://web.stanford.edu/~jurafsky/slp3/.

Kadavath, Saurav, Tom Conerly, Amanda Askell, et al. 2022. “Language Models (Mostly) Know What They Know.” arXiv Preprint arXiv:2207.05221.

Kalajdzievski, Damjan. 2023. A Rank Stabilization Scaling Factor for Fine-Tuning with LoRA. https://arxiv.org/abs/2312.03732.

Kaplan, Jared, Sam McCandlish, Tom Henighan, Tom B. Brown, et al. 2020. “Scaling Laws for Neural Language Models.” arXiv Preprint arXiv:2001.08361. https://arxiv.org/abs/2001.08361.

Kapoor, Sayash, and Arvind Narayanan. 2023. “Leakage and the Reproducibility Crisis in Machine-Learning-Based Science.” Patterns 4 (9): 100804.

Karpathy, Andrej. 2024. Minbpe: Minimal, Clean Code for the Byte Pair Encoding Algorithm. https://github.com/karpathy/minbpe.

Karpukhin, Vladimir, Barlas Oğuz, Sewon Min, et al. 2020. “Dense Passage Retrieval for Open-Domain Question Answering.” Proceedings of EMNLP.

Katz, Slava M. 1987. “Estimation of Probabilities from Sparse Data for the Language Model Component of a Speech Recognizer.” IEEE Transactions on Acoustics, Speech, and Signal Processing 35 (3): 400–401.

Kazemnejad, Amirhossein, Inkit Padhi, Karthikeyan Natesan Ramamurthy, Payel Das, and Siva Reddy. 2023. “The Impact of Positional Encoding on Length Generalization in Transformers.” Advances in Neural Information Processing Systems 36.

Keskar, Nitish Shirish, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. 2019. “CTRL: A Conditional Transformer Language Model for Controllable Generation.” arXiv Preprint arXiv:1909.05858. https://arxiv.org/abs/1909.05858.

Khattab, Omar, and Matei Zaharia. 2020. “ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT.” Proceedings of SIGIR.

Kirkpatrick, James, Razvan Pascanu, Neil Rabinowitz, et al. 2017. “Overcoming Catastrophic Forgetting in Neural Networks.” Proceedings of the National Academy of Sciences 114 (13): 3521–26. https://arxiv.org/abs/1612.00796.

Klie, Jan-Christoph, Bonnie Webber, and Iryna Gurevych. 2023. “Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future.” Computational Linguistics 49 (1): 157–98.

Kneser, Reinhard, and Hermann Ney. 1995. “Improved Backing-Off for m-Gram Language Modeling.” International Conference on Acoustics, Speech, and Signal Processing, 181–84.

Kocmi, Tom, Vilém Zouhar, Christian Federmann, and Matt Post. 2024. “Navigating the Metrics Maze: Reconciling Score Magnitudes and Accuracies.” Proceedings of ACL.

Koehn, Philipp, and Rebecca Knowles. 2017. “Six Challenges for Neural Machine Translation.” Proceedings of the First Workshop on Neural Machine Translation.

Koehn, Philipp, Franz Josef Och, and Daniel Marcu. 2003. “Statistical Phrase-Based Translation.” Proceedings of NAACL-HLT.

Kojima, Takeshi, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022a. “Large Language Models Are Zero-Shot Reasoners.” Advances in Neural Information Processing Systems.

Kojima, Takeshi, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022b. “Large Language Models Are Zero-Shot Reasoners.” Advances in Neural Information Processing Systems (NeurIPS) 35: 22199–213. https://arxiv.org/abs/2205.11916.

Krippendorff, Klaus. 2018. Content Analysis: An Introduction to Its Methodology. 4th ed. SAGE Publications.

Kryściński, Wojciech, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. “Neural Text Summarization: A Critical Evaluation.” Proceedings of EMNLP-IJCNLP.

Kudo, Taku. 2018. “Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates.” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 66–75. https://aclanthology.org/P18-1007/.

Kudo, Taku, and John Richardson. 2018. “SentencePiece: A Simple and Language Independent Subword Tokenizer and Detokenizer for Neural Text Processing.” Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 66–71. https://aclanthology.org/D18-2012/.

Kumar, Ananya, Percy Liang, and Tengyu Ma. 2019. “Verified Uncertainty Calibration.” Advances in Neural Information Processing Systems.

Kwa, Thomas, Ben West, Joel Becker, et al. 2025. Measuring AI Ability to Complete Long Software Tasks. https://arxiv.org/abs/2503.14499.

Kwon, Woosuk, Zhuohan Li, Siyuan Zhuang, et al. 2023. “Efficient Memory Management for Large Language Model Serving with PagedAttention.” Proceedings of the 29th Symposium on Operating Systems Principles (SOSP).

Lafferty, John, Andrew McCallum, and Fernando C. N. Pereira. 2001. “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data.” Proceedings of ICML.

Lambert, Nathan, Jacob Morrison, Valentina Pyatkin, et al. 2025. “Tülu 3: Pushing Frontiers in Open Language Model Post-Training.” arXiv Preprint arXiv:2411.15124. https://arxiv.org/abs/2411.15124.

Lample, Guillaume, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. “Neural Architectures for Named Entity Recognition.” Proceedings of NAACL-HLT.

Landis, J. Richard, and Gary G. Koch. 1977. “The Measurement of Observer Agreement for Categorical Data.” Biometrics 33 (1): 159–74.

Lang, Ken. 1995. “NewsWeeder: Learning to Filter Netnews.” Proceedings of the 12th International Conference on Machine Learning (ICML), 331–39. https://doi.org/10.1016/B978-1-55860-377-6.50048-7.

Lanham, Tamera, Anna Chen, Ansh Radhakrishnan, et al. 2023. “Measuring Faithfulness in Chain-of-Thought Reasoning.” arXiv Preprint arXiv:2307.13702. https://arxiv.org/abs/2307.13702.

Lau, Jey Han, and Timothy Baldwin. 2016. “An Empirical Evaluation of Doc2vec with Practical Insights into Document Embedding Generation.” Proceedings of the 1st Workshop on Representation Learning for NLP, 78–86. https://aclanthology.org/W16-1609/.

Lawsen, Alex. 2025. “Comment on the Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity.” arXiv Preprint arXiv:2506.09250. https://arxiv.org/abs/2506.09250.

Le, Quoc, and Tomas Mikolov. 2014. “Distributed Representations of Sentences and Documents.” Proceedings of the 31st International Conference on Machine Learning (ICML), 1188–96. https://arxiv.org/abs/1405.4053.

Lee, Daniel D., and H. Sebastian Seung. 1999. “Learning the Parts of Objects by Non-Negative Matrix Factorization.” Nature 401 (6755): 788–91. https://doi.org/10.1038/44565.

Lee, Katherine, Daphne Ippolito, Andrew Nystrom, et al. 2022. “Deduplicating Training Data Makes Language Models Better.” Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL), 8424–45. https://arxiv.org/abs/2107.06499.

Lester, Brian, Rami Al-Rfou, and Noah Constant. 2021. “The Power of Scale for Parameter-Efficient Prompt Tuning.” Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3045–59. https://arxiv.org/abs/2104.08691.

Levesque, Hector J., Ernest Davis, and Leora Morgenstern. 2012. “The Winograd Schema Challenge.” Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning (KR), 552–61.

Leviathan, Yaniv, Matan Kalman, and Yossi Matias. 2023. “Fast Inference from Transformers via Speculative Decoding.” Proceedings of the 40th International Conference on Machine Learning (ICML).

Levy, Omer, and Yoav Goldberg. 2014a. “Linguistic Regularities in Sparse and Explicit Word Representations.” Proceedings of the Eighteenth Conference on Computational Natural Language Learning (CoNLL), 171–80. https://aclanthology.org/W14-1618/.

Levy, Omer, and Yoav Goldberg. 2014b. “Neural Word Embedding as Implicit Matrix Factorization.” Advances in Neural Information Processing Systems (NeurIPS) 27. https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization.

Levy, Omer, Yoav Goldberg, and Ido Dagan. 2015. “Improving Distributional Similarity with Lessons Learned from Word Embeddings.” Transactions of the Association for Computational Linguistics 3: 211–25. https://aclanthology.org/Q15-1016/.

Lewis, Patrick, Ethan Perez, Aleksandra Piktus, et al. 2020. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” Advances in Neural Information Processing Systems 33.

Li, Jingye, Hao Fei, Jiang Liu, et al. 2022. “Unified Named Entity Recognition as Word-Word Relation Classification.” Proceedings of the AAAI Conference on Artificial Intelligence.

Li, Xiaoya, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu, and Jiwei Li. 2020. “A Unified MRC Framework for Named Entity Recognition.” Proceedings of ACL.

Liang, Percy, Rishi Bommasani, Tony Lee, et al. 2023. “Holistic Evaluation of Language Models.” Transactions on Machine Learning Research.

Lightman, Hunter, Vineet Kosaraju, Yura Burda, et al. 2024. “Let’s Verify Step by Step.” International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2305.20050.

Lignos, Constantine, Nolan Holley, Chester Palen-Michel, and Jonne Sleem. 2022. “Toward More Meaningful Resources for Lower-Resourced Languages.” Findings of the Association for Computational Linguistics: ACL 2022, 523–32.

Lin, Chin-Yew. 2004. “ROUGE: A Package for Automatic Evaluation of Summaries.” Text Summarization Branches Out.

Lin, Ji, Jiaming Tang, Haotian Tang, et al. 2024. “AWQ: Activation-Aware Weight Quantization for LLM Compression and Acceleration.” Proceedings of Machine Learning and Systems (MLSys).

Lin, Stephanie, Jacob Hilton, and Owain Evans. 2022. “Teaching Models to Express Their Uncertainty in Words.” Transactions on Machine Learning Research. https://openreview.net/forum?id=8s8K2UZGTZ.

Linzen, Tal. 2016. “Issues in Evaluating Semantic Spaces Using Word Analogies.” Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP, 13–18. https://arxiv.org/abs/1606.07736.

Liu, Haotian, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. “Visual Instruction Tuning.” Advances in Neural Information Processing Systems.

Liu, Jiachang, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022. “What Makes Good in-Context Examples for GPT-3?” Proceedings of Deep Learning Inside Out (DeeLIO).

Liu, Nelson F., Kevin Lin, John Hewitt, et al. 2024. “Lost in the Middle: How Language Models Use Long Contexts.” Transactions of the Association for Computational Linguistics 12: 157–73.

Liu, Yang, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Zhu, and Chenguang Zhu. 2023. “G-Eval: NLG Evaluation Using GPT-4 with Better Human Alignment.” Proceedings of EMNLP.

Lloyd, Stuart P. 1982. “Least Squares Quantization in PCM.” IEEE Transactions on Information Theory 28 (2): 129–37. https://doi.org/10.1109/TIT.1982.1056489.

Longpre, Shayne, Robert Mahari, Anthony Chen, et al. 2024. “A Large-Scale Audit of Dataset Licensing and Attribution in AI.” Nature Machine Intelligence 6 (8): 975–87.

Lowell, David, Zachary C. Lipton, and Byron C. Wallace. 2019. “Practical Obstacles to Deploying Active Learning.” Proceedings of EMNLP-IJCNLP. https://aclanthology.org/D19-1003/.

Lu, Yao, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022. “Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity.” Proceedings of ACL.

Lü, Linyuan, Zi-Ke Zhang, and Tao Zhou. 2010. “Zipf’s Law Leads to Heaps’ Law: Analyzing Their Relation in Finite-Size Systems.” PLoS ONE 5 (12): e14139. https://doi.org/10.1371/journal.pone.0014139.

Luo, Yun, Zhen Yang, Fandong Meng, Yafu Li, Jie Zhou, and Yue Zhang. 2025. “An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-Tuning.” arXiv Preprint arXiv:2308.08747. https://arxiv.org/abs/2308.08747.

Madaan, Aman, Niket Tandon, Prakhar Gupta, et al. 2023. “Self-Refine: Iterative Refinement with Self-Feedback.” Advances in Neural Information Processing Systems.

Magar, Inbal, and Roy Schwartz. 2022. “Data Contamination: From Memorization to Exploitation.” Proceedings of ACL.

Malkov, Yu A., and Dmitry A. Yashunin. 2018. “Efficient and Robust Approximate Nearest Neighbor Search Using Hierarchical Navigable Small World Graphs.” IEEE Transactions on Pattern Analysis and Machine Intelligence.

Manku, Gurmeet Singh, Arvind Jain, and Anish Das Sarma. 2007. “Detecting Near-Duplicates for Web Crawling.” Proceedings of the 16th International Conference on World Wide Web (WWW), 141–50. https://research.google/pubs/detecting-near-duplicates-for-web-crawling/.

Manning, Christopher D., Prabhakar Raghavan, and Hinrich Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press. https://nlp.stanford.edu/IR-book/.

Marcus, Mitchell P., Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. “Building a Large Annotated Corpus of English: The Penn Treebank.” Computational Linguistics 19 (2): 313–30.

Maynez, Joshua, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. “On Faithfulness and Factuality in Abstractive Summarization.” Proceedings of ACL.

McInnes, Leland, and John Healy. 2017. “Accelerated Hierarchical Density Based Clustering.” 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 33–42. https://doi.org/10.1109/ICDMW.2017.12.

McInnes, Leland, John Healy, and James Melville. 2018. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. https://arxiv.org/abs/1802.03426.

McNemar, Quinn. 1947. “Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages.” Psychometrika 12 (2): 153–57.

Meister, Clara, Ryan Cotterell, and Tim Vieira. 2020. “If Beam Search Is the Answer, What Was the Question?” Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2173–85.

Michel, Paul, Omer Levy, and Graham Neubig. 2019. “Are Sixteen Heads Really Better Than One?” Advances in Neural Information Processing Systems (NeurIPS) 32.

Mihalcea, Rada, and Paul Tarau. 2004. “TextRank: Bringing Order into Text.” Proceedings of EMNLP.

Mikolov, Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. https://arxiv.org/abs/1301.3781.

Mikolov, Tomáš, Martin Karafiát, Lukáš Burget, Jan Černocký, and Sanjeev Khudanpur. 2010. “Recurrent Neural Network Based Language Model.” Interspeech, 1045–48.

Mikolov, Tomas, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. “Distributed Representations of Words and Phrases and Their Compositionality.” Advances in Neural Information Processing Systems (NeurIPS) 26. https://arxiv.org/abs/1310.4546.

Miller, Evan. 2024. Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations. https://arxiv.org/abs/2411.00640.

Min, Sewon, Xinxi Lyu, Ari Holtzman, et al. 2022. “Rethinking the Role of Demonstrations: What Makes in-Context Learning Work?” Proceedings of EMNLP.

Mitchell, Margaret, Simone Wu, Andrew Zaldivar, et al. 2019. “Model Cards for Model Reporting.” Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*).

Modarressi, Ali, Hanieh Deilamsalehy, Franck Dernoncourt, et al. 2025. “NoLiMa: Long-Context Evaluation Beyond Literal Matching.” Proceedings of ICML.

Mu, Jiaqi, Suma Bhat, and Pramod Viswanath. 2018. “All-but-the-Top: Simple and Effective Postprocessing for Word Representations.” arXiv Preprint arXiv:1702.01417v2. https://arxiv.org/abs/1702.01417.

Muennighoff, Niklas, Alexander M. Rush, Boaz Barak, et al. 2023. “Scaling Data-Constrained Language Models.” Advances in Neural Information Processing Systems 36. https://arxiv.org/abs/2305.16264.

Muennighoff, Niklas, Zitong Yang, Weijia Shi, et al. 2025. “S1: Simple Test-Time Scaling.” arXiv Preprint arXiv:2501.19393. https://arxiv.org/abs/2501.19393.

Murray, Kenton, and David Chiang. 2018. “Correcting Length Bias in Neural Machine Translation.” Proceedings of the Third Conference on Machine Translation, 212–23.

Narayan, Shashi, Shay B. Cohen, and Mirella Lapata. 2018. “Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization.” Proceedings of EMNLP.

Nguyen, Minh, Andrew Baker, Clement Neo, Allen Roush, Andreas Kirsch, and Ravid Shwartz-Ziv. 2024. “Turning up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs.” arXiv Preprint arXiv:2407.01082.

Niculescu-Mizil, Alexandru, and Rich Caruana. 2005. “Predicting Good Probabilities with Supervised Learning.” Proceedings of the 22nd International Conference on Machine Learning (ICML ’05) (New York, NY, USA), 625–32. https://doi.org/10.1145/1102351.1102430.

Nissim, Malvina, Rik van Noord, and Rob van der Goot. 2020. “Fair Is Better Than Sensational: Man Is to Doctor as Woman Is to Doctor.” Computational Linguistics 46 (2): 487–97. https://arxiv.org/abs/1905.09866.

Nixon, Jeremy, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran. 2019. “Measuring Calibration in Deep Learning.” CVPR Workshops.

Nogueira, Rodrigo, and Kyunghyun Cho. 2019. “Passage Re-Ranking with BERT.” arXiv Preprint arXiv:1901.04085.

Northcutt, Curtis G., Anish Athalye, and Jonas Mueller. 2021. “Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks.” Proceedings of the NeurIPS Track on Datasets and Benchmarks.

Northcutt, Curtis G., Lu Jiang, and Isaac L. Chuang. 2021. “Confident Learning: Estimating Uncertainty in Dataset Labels.” Journal of Artificial Intelligence Research 70: 1373–411.

Oflazer, Kemal. 1994. “Two-Level Description of Turkish Morphology.” Literary and Linguistic Computing 9 (2): 137–48. https://doi.org/10.1093/llc/9.2.137.

Olsson, Catherine, Nelson Elhage, Neel Nanda, et al. 2022. “In-Context Learning and Induction Heads.” Transformer Circuits Thread. https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html.

Ong, Isaac, Amjad Almahairi, Vincent Wu, et al. 2024. “RouteLLM: Learning to Route LLMs with Preference Data.” arXiv Preprint arXiv:2406.18665.

OpenAI. 2024. Learning to Reason with LLMs. OpenAI blog. https://openai.com/index/learning-to-reason-with-llms/.

Oren, Yonatan, Nicole Meister, Niladri S. Chatterji, Faisal Ladhak, and Tatsunori B. Hashimoto. 2024. “Proving Test Set Contamination in Black-Box Language Models.” International Conference on Learning Representations (ICLR).

Ouyang, Long et al. 2022. “Training Language Models to Follow Instructions with Human Feedback.” Advances in Neural Information Processing Systems 35.

Packer, Charles, Sarah Wooders, Kevin Lin, et al. 2023. “MemGPT: Towards LLMs as Operating Systems.” arXiv Preprint arXiv:2310.08560.

Pan, Jane, Tianyu Gao, Howard Chen, and Danqi Chen. 2023. “What in-Context Learning "Learns" in-Context: Disentangling Task Recognition and Task Learning.” Findings of the Association for Computational Linguistics: ACL 2023.

Pan, Xiaoman, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, and Heng Ji. 2017. “Cross-Lingual Name Tagging and Linking for 282 Languages.” Proceedings of ACL.

Pangakis, Nicholas, Samuel Wolken, and Neil Fasching. 2023. Automated Annotation with Generative AI Requires Validation. https://arxiv.org/abs/2306.00176.

Panickssery, Arjun, Samuel R. Bowman, and Shi Feng. 2024. “LLM Evaluators Recognize and Favor Their Own Generations.” Advances in Neural Information Processing Systems (NeurIPS) 37. https://proceedings.neurips.cc/paper_files/paper/2024/hash/7f1f0218e45f5414c79c0679633e47bc-Abstract-Conference.html.

Papineni, Kishore, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. “BLEU: A Method for Automatic Evaluation of Machine Translation.” Proceedings of ACL.

Park, Kanghee, Jiayu Wang, Taylor Berg-Kirkpatrick, Nadia Polikarpova, and Loris D’Antoni. 2024. “Grammar-Aligned Decoding.” Advances in Neural Information Processing Systems.

Pascanu, Razvan, Tomas Mikolov, and Yoshua Bengio. 2013. “On the Difficulty of Training Recurrent Neural Networks.” Proceedings of the 30th International Conference on Machine Learning (ICML), 1310–18.

Pedregosa, Fabian, Gaël Varoquaux, Alexandre Gramfort, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–30. https://jmlr.org/papers/v12/pedregosa11a.html.

Penedo, Guilherme, Hynek Kydlı́ček, Loubna Ben allal, et al. 2024. “The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale.” Advances in Neural Information Processing Systems 37 (NeurIPS 2024) Datasets and Benchmarks Track. https://arxiv.org/abs/2406.17557.

Penedo, Guilherme, Quentin Malartic, Daniel Hesslow, et al. 2023. “The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only.” Advances in Neural Information Processing Systems 36 (NeurIPS 2023) Datasets and Benchmarks Track. https://arxiv.org/abs/2306.01116.

Peng, Bowen, Jeffrey Quesnelle, Honglu Fan, and Enrico Shippole. 2024. “YaRN: Efficient Context Window Extension of Large Language Models.” International Conference on Learning Representations. https://arxiv.org/abs/2309.00071.

Pennington, Jeffrey, Richard Socher, and Christopher D. Manning. 2014. “GloVe: Global Vectors for Word Representation.” Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–43. https://nlp.stanford.edu/pubs/glove.pdf.

Perez, Fábio, and Ian Ribeiro. 2022. “Ignore Previous Prompt: Attack Techniques for Language Models.” arXiv Preprint arXiv:2211.09527.

Peters, Matthew E., Mark Neumann, Mohit Iyyer, et al. 2018. “Deep Contextualized Word Representations.” Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2227–37. https://arxiv.org/abs/1802.05365.

Petrov, Aleksandar, Emanuele La Malfa, Philip H. S. Torr, and Adel Bibi. 2023. “Language Model Tokenizers Introduce Unfairness Between Languages.” Advances in Neural Information Processing Systems 36. https://arxiv.org/abs/2305.15425.

Pfau, Jacob, William Merrill, and Samuel R. Bowman. 2024. “Let’s Think Dot by Dot: Hidden Computation in Transformer Language Models.” Conference on Language Modeling (COLM). https://arxiv.org/abs/2404.15758.

Pham, Chau Minh, Alexander Hoyle, Simeng Sun, Philip Resnik, and Mohit Iyyer. 2024. “TopicGPT: A Prompt-Based Topic Modeling Framework.” Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics.

Plank, Barbara. 2022. “The ‘Problem’ of Human Label Variation: On Ground Truth in Data, Modeling and Evaluation.” Proceedings of EMNLP.

Platt, John C. 1999. “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods.” In Advances in Large Margin Classifiers, edited by Alexander J. Smola, Peter L. Bartlett, Bernhard Schölkopf, and Dale Schuurmans. MIT Press.

Pope, Reiner, Sholto Douglas, Aakanksha Chowdhery, et al. 2023. “Efficiently Scaling Transformer Inference.” Proceedings of Machine Learning and Systems (MLSys).

Popović, Maja. 2015. “chrF: Character n-Gram f-Score for Automatic MT Evaluation.” Proceedings of the Tenth Workshop on Statistical Machine Translation.

Porter, Martin F. 1980. “An Algorithm for Suffix Stripping.” Program 14 (3): 130–37. https://doi.org/10.1108/eb046814.

Post, Matt. 2018. “A Call for Clarity in Reporting BLEU Scores.” Proceedings of the Third Conference on Machine Translation.

Press, Ofir, Noah A. Smith, and Mike Lewis. 2022. “Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.” International Conference on Learning Representations (ICLR).

Provilkov, Ivan, Dmitrii Emelianenko, and Elena Voita. 2020. “BPE-Dropout: Simple and Effective Subword Regularization.” Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 1882–92. https://doi.org/10.18653/v1/2020.acl-main.170.

Pustejovsky, James, and Amber Stubbs. 2012. Natural Language Annotation for Machine Learning. O’Reilly Media.

Qin, Yujia, Shihao Liang, Yining Ye, et al. 2024. “ToolLLM: Facilitating Large Language Models to Master 16000+ Real-World APIs.” Proceedings of ICLR.

Radford, Alec, Jong Wook Kim, Chris Hallacy, et al. 2021. “Learning Transferable Visual Models from Natural Language Supervision.” Proceedings of ICML.

Radford, Alec, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language Models Are Unsupervised Multitask Learners. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf.

Rafailov, Rafael, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn. 2023. “Direct Preference Optimization: Your Language Model Is Secretly a Reward Model.” Advances in Neural Information Processing Systems 36. https://arxiv.org/abs/2305.18290.

Raffel, Colin, Noam Shazeer, Adam Roberts, et al. 2020. “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.” Journal of Machine Learning Research 21 (140): 1–67.

Rahimi, Afshin, Yuan Li, and Trevor Cohn. 2019. “Massively Multilingual Transfer for NER.” Proceedings of ACL.

Rasmussen, Preston, Pavlo Paliychuk, Travis Beauvais, Jack Ryan, and Daniel Chalef. 2025. Zep: A Temporal Knowledge Graph Architecture for Agent Memory. https://arxiv.org/abs/2501.13956.

Ratinov, Lev, and Dan Roth. 2009. “Design Challenges and Misconceptions in Named Entity Recognition.” Proceedings of CoNLL.

Ratner, Alexander, Stephen H. Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017. “Snorkel: Rapid Training Data Creation with Weak Supervision.” Proceedings of the VLDB Endowment 11 (3): 269–82.

Recht, Benjamin, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. 2019. “Do ImageNet Classifiers Generalize to ImageNet?” Proceedings of ICML.

Řehůřek, Radim, and Petr Sojka. 2010. “Software Framework for Topic Modelling with Large Corpora.” Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks, 45–50. https://radimrehurek.com/gensim/.

Rei, Ricardo, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020. “COMET: A Neural Framework for MT Evaluation.” Proceedings of EMNLP.

Reimers, Nils, and Iryna Gurevych. 2019. “Sentence-BERT: Sentence Embeddings Using Siamese BERT-Networks.” Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3982–92. https://arxiv.org/abs/1908.10084.

Reynolds, Laria, and Kyle McDonell. 2021. “Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm.” Extended Abstracts of CHI.

Robertson, Stephen. 2004. “Understanding Inverse Document Frequency: On Theoretical Arguments for IDF.” Journal of Documentation 60 (5): 503–20. https://doi.org/10.1108/00220410410560582.

Robertson, Stephen, and Hugo Zaragoza. 2009. “The Probabilistic Relevance Framework: BM25 and Beyond.” Foundations and Trends in Information Retrieval 3 (4): 333–89. https://doi.org/10.1561/1500000019.

Röder, Michael, Andreas Both, and Alexander Hinneburg. 2015. “Exploring the Space of Topic Coherence Measures.” Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, 399–408.

Roelofs, Rebecca, Nicholas Cain, Jonathon Shlens, and Michael C. Mozer. 2022. “Mitigating Bias in Calibration Error Estimation.” Proceedings of the 25th International Conference on Artificial Intelligence and Statistics.

Rohrbach, Anna, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. 2018. “Object Hallucination in Image Captioning.” Proceedings of EMNLP.

Rong, Xin. 2014. Word2vec Parameter Learning Explained. https://arxiv.org/abs/1411.2738.

Rosenberg, Andrew, and Julia Hirschberg. 2007. “V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure.” Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 410–20. https://aclanthology.org/D07-1043/.

Rousseeuw, Peter J. 1987. “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis.” Journal of Computational and Applied Mathematics 20: 53–65. https://doi.org/10.1016/0377-0427(87)90125-7.

Ruder, Sebastian. 2016. On Word Embeddings. https://www.ruder.io/word-embeddings-1/.

Rust, Phillip, Jonas Pfeiffer, Ivan Vulić, Sebastian Ruder, and Iryna Gurevych. 2021. “How Good Is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models.” Proceedings of ACL-IJCNLP.

Safaya, Ali, Emirhan Kurtuluş, Arda Goktogan, and Deniz Yuret. 2022. “Mukayese: Turkish NLP Strikes Back.” Findings of ACL.

Sainz, Oscar, Jon Ander Campos, Iker García-Ferrero, Julen Etxaniz, Oier Lopez de Lacalle, and Eneko Agirre. 2023. “NLP Evaluation in Trouble: On the Need to Measure LLM Data Contamination for Each Benchmark.” Findings of EMNLP.

Salton, Gerard, and Christopher Buckley. 1988. “Term-Weighting Approaches in Automatic Text Retrieval.” Information Processing & Management 24 (5): 513–23. https://doi.org/10.1016/0306-4573(88)90021-0.

Sambasivan, Nithya, Shivani Kapania, Hannah Highfill, Diana Akrong, Praveen Paritosh, and Lora M. Aroyo. 2021. “‘Everyone Wants to Do the Model Work, Not the Data Work’: Data Cascades in High-Stakes AI.” Proceedings of the CHI Conference on Human Factors in Computing Systems.

Schaeffer, Rylan, Brando Miranda, and Sanmi Koyejo. 2023. “Are Emergent Abilities of Large Language Models a Mirage?” Advances in Neural Information Processing Systems 36. https://arxiv.org/abs/2304.15004.

Schick, Timo, Jane Dwivedi-Yu, Roberto Dessì, et al. 2023. “Toolformer: Language Models Can Teach Themselves to Use Tools.” Advances in Neural Information Processing Systems.

Schnabel, Tobias, Igor Labutov, David Mimno, and Thorsten Joachims. 2015. “Evaluation Methods for Unsupervised Word Embeddings.” Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), 298–307. https://aclanthology.org/D15-1036/.

Schubert, Erich. 2023. “Stop Using the Elbow Criterion for k-Means and How to Choose the Number of Clusters Instead.” ACM SIGKDD Explorations Newsletter 25 (1): 36–42. https://doi.org/10.1145/3606274.3606278.

Schulhoff, Sander, Michael Ilie, Nishant Balepur, et al. 2025. “The Prompt Report: A Systematic Survey of Prompt Engineering Techniques.” arXiv Preprint arXiv:2406.06608. https://arxiv.org/abs/2406.06608.

Schulman, John, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017. “Proximal Policy Optimization Algorithms.” arXiv Preprint arXiv:1707.06347. https://arxiv.org/abs/1707.06347.

Schuster, Mike, and Kaisuke Nakajima. 2012. “Japanese and Korean Voice Search.” 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5149–52. https://doi.org/10.1109/ICASSP.2012.6289079.

Schweter, Stefan. 2020. BERTurk — BERT Models for Turkish. V. 1.0.0. Zenodo, released April. https://doi.org/10.5281/zenodo.3770924.

Sclar, Melanie, Yejin Choi, Yulia Tsvetkov, and Alane Suhr. 2024. “Quantifying Language Models’ Sensitivity to Spurious Features in Prompt Design.” Proceedings of ICLR.

See, Abigail, Peter J. Liu, and Christopher D. Manning. 2017. “Get to the Point: Summarization with Pointer-Generator Networks.” Proceedings of ACL.

Sennrich, Rico, Barry Haddow, and Alexandra Birch. 2016a. “Improving Neural Machine Translation Models with Monolingual Data.” Proceedings of ACL.

Sennrich, Rico, Barry Haddow, and Alexandra Birch. 2016b. “Neural Machine Translation of Rare Words with Subword Units.” Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, 1715–25. https://aclanthology.org/P16-1162/.

Settles, Burr. 2009. Active Learning Literature Survey. Computer Sciences Technical Report 1648. University of Wisconsin–Madison.

Shannon, Claude E. 1951. “Prediction and Entropy of Printed English.” Bell System Technical Journal 30 (1): 50–64.

Shao, Zhihong, Peiyi Wang, Qihao Zhu, et al. 2024. “DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.” arXiv Preprint arXiv:2402.03300. https://arxiv.org/abs/2402.03300.

Shazeer, Noam. 2019. “Fast Transformer Decoding: One Write-Head Is All You Need.” arXiv Preprint arXiv:1911.02150. https://arxiv.org/abs/1911.02150.

Shazeer, Noam. 2020. GLU Variants Improve Transformer. https://arxiv.org/abs/2002.05202.

Shazeer, Noam, Azalia Mirhoseini, Krzysztof Maziarz, et al. 2017. “Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.” International Conference on Learning Representations (ICLR).

Shi, Freda, Xinyun Chen, Kanishka Misra, et al. 2023. “Large Language Models Can Be Easily Distracted by Irrelevant Context.” Proceedings of ICML.

Shi, Tianneng, Jingxuan He, Zhun Wang, et al. 2025. “Progent: Securing AI Agents with Privilege Control.” arXiv Preprint arXiv:2504.11703.

Shinn, Noah, Federico Cassano, Edward Berman, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. 2023. “Reflexion: Language Agents with Verbal Reinforcement Learning.” Advances in Neural Information Processing Systems (NeurIPS) 36. https://arxiv.org/abs/2303.11366.

Shojaee, Parshin, Iman Mirzadeh, Keivan Alizadeh, Maxwell Horton, Samy Bengio, and Mehrdad Farajtabar. 2025. “The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity.” arXiv Preprint arXiv:2506.06941. https://arxiv.org/abs/2506.06941.

Shumailov, Ilia, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson, and Yarin Gal. 2024. “AI Models Collapse When Trained on Recursively Generated Data.” Nature 631: 755–59.

Silge, Julia, and David Robinson. 2017. Text Mining with r: A Tidy Approach. O’Reilly Media. https://www.tidytextmining.com/.

Singh, Shivalika, Yiyang Nan, Alex Wang, et al. 2025. The Leaderboard Illusion. https://arxiv.org/abs/2504.20879.

Snell, Charlie, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. 2025. “Scaling LLM Test-Time Compute Optimally Can Be More Effective Than Scaling Model Parameters.” International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2408.03314.

Snow, Rion, Brendan O’Connor, Daniel Jurafsky, and Andrew Y. Ng. 2008. “Cheap and Fast — but Is It Good? Evaluating Non-Expert Annotations for Natural Language Tasks.” Proceedings of EMNLP.

Soldaini, Luca, Rodney Kinney, Akshita Bhagia, et al. 2024. “Dolma: An Open Corpus of Three Trillion Tokens for Language Model Pretraining Research.” Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), 15725–88. https://arxiv.org/abs/2402.00159.

Spärck Jones, Karen. 1972. “A Statistical Interpretation of Term Specificity and Its Application in Retrieval.” Journal of Documentation 28 (1): 11–21. https://doi.org/10.1108/eb026526.

Sprague, Zayne, Fangcong Yin, Juan Diego Rodriguez, et al. 2025. “To CoT or Not to CoT? Chain-of-Thought Helps Mainly on Math and Symbolic Reasoning.” International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2409.12183.

Stanford Institute for Human-Centered AI. 2025. Artificial Intelligence Index Report 2025. Stanford University. https://hai.stanford.edu/ai-index/2025-ai-index-report.

Stanford Institute for Human-Centered AI. 2026. Artificial Intelligence Index Report 2026. Stanford University. https://hai.stanford.edu/ai-index/2026-ai-index-report.

Steck, Harald, Chaitanya Ekanadham, and Nathan Kallus. 2024. “Is Cosine-Similarity of Embeddings Really about Similarity?” Companion Proceedings of the ACM Web Conference 2024 (WWW ’24 Companion), 887–90. https://doi.org/10.1145/3589335.3651526.

Stiennon, Nisan, Long Ouyang, Jeffrey Wu, et al. 2020. “Learning to Summarize with Human Feedback.” Advances in Neural Information Processing Systems 33: 3008–21. https://arxiv.org/abs/2009.01325.

Su, Jianlin, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng Liu. 2024. “RoFormer: Enhanced Transformer with Rotary Position Embedding.” Neurocomputing 568: 127063.

Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le. 2014. “Sequence to Sequence Learning with Neural Networks.” Advances in Neural Information Processing Systems 27.

Sutton, Richard S. 2019. The Bitter Lesson. http://www.incompleteideas.net/IncIdeas/BitterLesson.html.

Tam, Zhi Rui, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-yi Lee, and Yun-Nung Chen. 2024. “Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models.” Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track. https://arxiv.org/abs/2408.02442.

Thakur, Nandan, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2021. “BEIR: A Heterogeneous Benchmark for Zero-Shot Evaluation of Information Retrieval Models.” NeurIPS Datasets and Benchmarks Track.

Tjong Kim Sang, Erik F., and Fien De Meulder. 2003. “Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition.” Proceedings of CoNLL.

Traag, Vincent A., Ludo Waltman, and Nees Jan van Eck. 2019. “From Louvain to Leiden: Guaranteeing Well-Connected Communities.” Scientific Reports 9 (1): 5233.

Tramèr, Florian, Nicholas Carlini, Wieland Brendel, and Aleksander Mądry. 2020. “On Adaptive Attacks to Adversarial Example Defenses.” Advances in Neural Information Processing Systems (NeurIPS).

Tunstall, Lewis, Nils Reimers, Unso Eun Seo Jo, et al. 2022. Efficient Few-Shot Learning Without Prompts. https://arxiv.org/abs/2209.11055.

Turpin, Miles, Julian Michael, Ethan Perez, and Samuel R. Bowman. 2023. “Language Models Don’t Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting.” Advances in Neural Information Processing Systems (NeurIPS). https://arxiv.org/abs/2305.04388.

Uber Engineering. 2018. COTA: Improving Uber Customer Care with NLP and Machine Learning. https://www.uber.com/blog/cota/.

Uesato, Jonathan, Nate Kushman, Ramana Kumar, et al. 2022. “Solving Math Word Problems with Process- and Outcome-Based Feedback.” arXiv Preprint arXiv:2211.14275. https://arxiv.org/abs/2211.14275.

Uma, Alexandra N., Tommaso Fornaciari, Dirk Hovy, Silviu Paun, Barbara Plank, and Massimo Poesio. 2021. “Learning from Disagreement: A Survey.” Journal of Artificial Intelligence Research 72: 1385–470.

van den Goorbergh, Ruben, Maarten van Smeden, Dirk Timmerman, and Ben Van Calster. 2022. “The Harm of Class Imbalance Corrections for Risk Prediction Models: Illustration and Simulation Using Logistic Regression.” Journal of the American Medical Informatics Association 29 (9): 1525–34. https://doi.org/10.1093/jamia/ocac093.

Vaswani, Ashish, Noam Shazeer, Niki Parmar, et al. 2017. “Attention Is All You Need.” Advances in Neural Information Processing Systems 30.

Vilar, David, Markus Freitag, Colin Cherry, Jiaming Luo, Viresh Ratnakar, and George Foster. 2023. “Prompting PaLM for Translation: Assessing Strategies and Performance.” Proceedings of ACL.

Vinh, Nguyen Xuan, Julien Epps, and James Bailey. 2010. “Information Theoretic Measures for Clusterings Comparison: Variants, Properties, Normalization and Correction for Chance.” Journal of Machine Learning Research 11: 2837–54. https://jmlr.org/papers/v11/vinh10a.html.

Viterbi, Andrew J. 1967. “Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm.” IEEE Transactions on Information Theory 13 (2): 260–69.

Voita, Elena. 2020. NLP Course For You. https://lena-voita.github.io/nlp_course.html.

Wang, Liang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei. 2024. “Multilingual E5 Text Embeddings: A Technical Report.” arXiv Preprint arXiv:2402.05672.

Wang, Peiyi, Lei Li, Liang Chen, et al. 2024. “Large Language Models Are Not Fair Evaluators.” Proceedings of ACL.

Wang, Xuezhi, Jason Wei, Dale Schuurmans, et al. 2023. “Self-Consistency Improves Chain of Thought Reasoning in Language Models.” International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2203.11171.

Wang, Yizhong, Yeganeh Kordi, Swaroop Mishra, et al. 2023. “Self-Instruct: Aligning Language Models with Self-Generated Instructions.” Proceedings of ACL.

Warner, Benjamin, Antoine Chaffin, Benjamin Clavié, et al. 2025. “Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.” Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics. https://arxiv.org/abs/2412.13663.

Wei, Jason, Maarten Bosma, Vincent Y. Zhao, et al. 2022. “Finetuned Language Models Are Zero-Shot Learners.” International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2109.01652.

Wei, Jason, Xuezhi Wang, Dale Schuurmans, et al. 2022. “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.” Advances in Neural Information Processing Systems (NeurIPS) 35: 24824–37. https://arxiv.org/abs/2201.11903.

Wei, Jiaheng, Zhaowei Zhu, Hao Cheng, Tongliang Liu, Gang Niu, and Yang Liu. 2022. “Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations.” International Conference on Learning Representations (ICLR).

Weinberger, Kilian, Anirban Dasgupta, John Langford, Alex Smola, and Josh Attenberg. 2009. “Feature Hashing for Large Scale Multitask Learning.” Proceedings of the 26th International Conference on Machine Learning (ICML), 1113–20. https://arxiv.org/abs/0902.2206.

Weizenbaum, Joseph. 1966. “ELIZA — a Computer Program for the Study of Natural Language Communication Between Man and Machine.” Communications of the ACM 9 (1): 36–45.

Weizenbaum, Joseph. 1976. Computer Power and Human Reason: From Judgment to Calculation. W. H. Freeman.

White, Colin, Samuel Dooley, Manley Roberts, et al. 2025. “LiveBench: A Challenging, Contamination-Limited LLM Benchmark.” International Conference on Learning Representations (ICLR). https://arxiv.org/abs/2406.19314.

Willard, Brandon T., and Rémi Louf. 2023. “Efficient Guided Generation for Large Language Models.” arXiv Preprint arXiv:2307.09702.

Willison, Simon. 2025. The Lethal Trifecta for AI Agents: Private Data, Untrusted Content, and External Communication. Simonwillison.net. https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/.

Wilson, Edwin B. 1927. “Probable Inference, the Law of Succession, and Statistical Inference.” Journal of the American Statistical Association 22 (158): 209–12.

Wu, Ledell, Fabio Petroni, Martin Josifoski, Sebastian Riedel, and Luke Zettlemoyer. 2020. “Scalable Zero-Shot Entity Linking with Dense Entity Retrieval.” Proceedings of EMNLP.

Xie, Sang Michael, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2022. “An Explanation of in-Context Learning as Implicit Bayesian Inference.” Proceedings of the Tenth International Conference on Learning Representations.

Xiong, Miao, Zhiyuan Hu, Xinyang Lu, et al. 2024. “Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs.” The Twelfth International Conference on Learning Representations (ICLR 2024). https://openreview.net/forum?id=gjeQKFxFpZ.

Xiong, Ruibin, Yunchang Yang, Di He, et al. 2020. “On Layer Normalization in the Transformer Architecture.” Proceedings of the 37th International Conference on Machine Learning (ICML), 10524–33.

Xue, Linting, Aditya Barua, Noah Constant, et al. 2022. “ByT5: Towards a Token-Free Future with Pre-Trained Byte-to-Byte Models.” Transactions of the Association for Computational Linguistics 10: 291–306. https://doi.org/10.1162/tacl_a_00461.

Yan, Xiaohui, Jiafeng Guo, Yanyan Lan, and Xueqi Cheng. 2013. “A Biterm Topic Model for Short Texts.” Proceedings of the 22nd International Conference on World Wide Web, 1445–56.

Yao, Shunyu, Noah Shinn, Pedram Razavi, and Karthik Narasimhan. 2024. τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains. https://arxiv.org/abs/2406.12045.

Yao, Shunyu, Dian Yu, Jeffrey Zhao, et al. 2023. “Tree of Thoughts: Deliberate Problem Solving with Large Language Models.” Advances in Neural Information Processing Systems.

Yao, Shunyu, Jeffrey Zhao, Dian Yu, et al. 2023. “ReAct: Synergizing Reasoning and Acting in Language Models.” Proceedings of ICLR.

Yin, Jianhua, and Jianyong Wang. 2014. “A Dirichlet Multinomial Mixture Model-Based Approach for Short Text Clustering.” Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 233–42.

Yu, Gyeong-In, Joo Seong Jeong, Geon-Woo Kim, Soojeong Kim, and Byung-Gon Chun. 2022. “Orca: A Distributed Serving System for Transformer-Based Generative Models.” 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI).

Yu, Juntao, Bernd Bohnet, and Massimo Poesio. 2020. “Named Entity Recognition as Dependency Parsing.” Proceedings of ACL.

Zadrozny, Bianca, and Charles Elkan. 2002. “Transforming Classifier Scores into Accurate Multiclass Probability Estimates.” Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’02) (New York, NY, USA), 694–99. https://doi.org/10.1145/775047.775151.

Zaratiana, Urchade, Nadi Tomeh, Pierre Holat, and Thierry Charnois. 2024. “GLiNER: Generalist Model for Named Entity Recognition Using Bidirectional Transformer.” Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics.

Zhai, Xiaohua, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. 2023. “Sigmoid Loss for Language Image Pre-Training.” Proceedings of ICCV.

Zhang, Aston, Zachary C. Lipton, Mu Li, and Alexander J. Smola. 2023. Dive into Deep Learning. Cambridge University Press. https://d2l.ai.

Zhang, Biao, and Rico Sennrich. 2019. “Root Mean Square Layer Normalization.” Advances in Neural Information Processing Systems (NeurIPS) 32. https://arxiv.org/abs/1910.07467.

Zhang, Biao, Philip Williams, Ivan Titov, and Rico Sennrich. 2020. “Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation.” Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL), 1628–39. https://aclanthology.org/2020.acl-main.148.

Zhang, Tianyi, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. “BERTScore: Evaluating Text Generation with BERT.” Proceedings of ICLR.

Zhang, Yuhao, Victor Zhong, Danqi Chen, Gabor Angeli, and Christopher D. Manning. 2017. “Position-Aware Attention and Supervised Data Improve Slot Filling.” Proceedings of EMNLP.

Zhao, Tony Z., Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021. “Calibrate Before Use: Improving Few-Shot Performance of Language Models.” Proceedings of the 38th International Conference on Machine Learning (ICML 2021), Proceedings of machine learning research, vol. 139: 12697–706. https://proceedings.mlr.press/v139/zhao21c.html.

Zheng, Lianmin, Wei-Lin Chiang, Ying Sheng, et al. 2023. “Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena.” Advances in Neural Information Processing Systems.

Zhou, Chunting, Pengfei Liu, Puxin Xu, et al. 2023. “LIMA: Less Is More for Alignment.” Advances in Neural Information Processing Systems 36. https://arxiv.org/abs/2305.11206.

Zhou, Wenxuan, Sheng Zhang, Yu Gu, Muhao Chen, and Hoifung Poon. 2024. “UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition.” Proceedings of the Twelfth International Conference on Learning Representations.

Ziems, Caleb, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, and Diyi Yang. 2024. “Can Large Language Models Transform Computational Social Science?” Computational Linguistics 50 (1): 237–91.

Zipf, George Kingsley. 1949. Human Behavior and the Principle of Least Effort. Addison-Wesley.

Zou, Andy, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson. 2023. “Universal and Transferable Adversarial Attacks on Aligned Language Models.” arXiv Preprint arXiv:2307.15043.