---
title: "Who cites Christopher E. Potts — page 11 — 9,352 citing papers"
description: "9,352 papers of the Elamur Research library cite the works of Christopher E. Among them: Rho-1: Not All Tokens Are What You Need…"
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---
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# Who cites Christopher E. Potts

11,943 citations of their works from 9,352 papers of our library (of the 149,439 whose bibliographies we read) · 1,871 with a page here, the most cited first · page 11 of 19 · 1001–1100 · [← Christopher E. Potts](https://md.research.elamur.ai/free/en/authors/christopher-e-potts/)

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1001. [**Rho-1: Not All Tokens Are What You Need arXiv:2404.07965**Zhenghao Lin, Zhibin Gou, Yeyun Gong, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-rho-1-all-tokens-need/)
1002. [**Simple linear attention language models balance the recall-throughput tradeoff arXiv:2402.18668**Simran Arora, Sabri Eyuboglu, Michael Zhang, et al.*2024 · 59 citations*](https://md.research.elamur.ai/free/en/papers/arora-2024-simple-linear-attention-language-models/)
1003. [**Temporal Reasoning in Natural Language Inference**Siddharth Vashishtha, Adam Poliak, Yash Kumar Lal, et al.*2020 · 59 citations*](https://md.research.elamur.ai/free/en/papers/vashishtha-2020-temporal-reasoning-natural-language-inference/)
1004. **[BIG-Bench Extra Hard](https://md.research.elamur.ai/free/en/papers/kazemi-2025-big-bench-extra-hard/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mehran Kazemi, Bahare Fatemi, Hritik Bansal, et al.*2025 · 58 citations*
1005. **[SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs](https://md.research.elamur.ai/free/en/papers/xu-2025-softcot-soft-chain-thought-efficient/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yige Xu, Xu Guo, Zhiwei Zeng, et al.*2025 · 58 citations*
1006. **[Bias in Large Language Models: Origin, Evaluation, and Mitigation](https://md.research.elamur.ai/free/en/papers/guo-2024-bias-large-language-models-origin/) [Electronics](https://md.research.elamur.ai/free/en/venues/electronics/)**Yufei Guo, Muzhe Guo, Juntao Su, et al.*2024 · 58 citations*
1007. [**Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla arXiv:2307.09458**Tom Lieberum, Matthew Rahtz, Janos Kramar, et al.*2023 · 58 citations*](https://md.research.elamur.ai/free/en/papers/lieberum-2023-circuit-analysis-interpretability-scale-evidence/)
1008. **[What Does My QA Model Know? Devising Controlled Probes Using Expert Knowledge](https://md.research.elamur.ai/free/en/papers/richardson-2020-what-does-my-qa-model/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Kyle Richardson, Ashish Sabharwal*2020 · 58 citations*
1009. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws arXiv:2401.00448**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 57 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
1010. [**Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedback arXiv:2303.05453**Hannah Rose Kirk, Bertie Vidgen, Paul Rottger, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-personalisation-within-bounds-risk-taxonomy/)
1011. [**SuperCLUE: A Comprehensive Chinese Large Language Model Benchmark arXiv:2307.15020**Liang Xu, Anqi Li, Lei Zhu, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-superclue-comprehensive-chinese-large/)
1012. [**Teaching Arithmetic to Small Transformers arXiv:2307.03381**Nayoung Lee, Kartik Sreenivasan, Jason D. Lee, et al.*2023 · 57 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-teaching-arithmetic-small-transformers/)
1013. **[Augmenting English Adjective Senses with Supersenses](https://md.research.elamur.ai/free/en/papers/tsvetkov-2014-augmenting-english-adjective-senses-supersenses/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yulia Tsvetkov, Nathan Schneider, Dirk Hovy, et al.*2014 · 57 citations*
1014. **[The Art of Saying No: Contextual Noncompliance in Language Models](https://md.research.elamur.ai/free/en/papers/brahman-2024-art-saying-no-contextual-noncompliance/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Faeze Brahman, Sachin Kumar, Vidhisha Balachandran, et al.*2024 · 56 citations*
1015. **[Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions](https://md.research.elamur.ai/free/en/papers/zelikman-2023-parsel-algorithmic-reasoning-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Eric Zelikman, Qian Huang, Gabriel Poesia, et al.*2023 · 56 citations*
1016. **[Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on Robustness](https://md.research.elamur.ai/free/en/papers/sanchez-2018-behavior-analysis-nli-models-uncovering/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ivan Sanchez, Jeff Mitchell, Sebastian Riedel*2018 · 56 citations*
1017. [**Was It Good? It Was Provocative.” Learning the Meaning of Scalar Adjectives**Marie-Catherine de Marneffe, Christopher D. Manning, Christopher Potts*2010 · 56 citations*](https://md.research.elamur.ai/free/en/papers/marneffe-2010-was-it-good-it-was/)
1018. [**A Reasoning-Focused Legal Retrieval Benchmark arXiv:2505.03970**Lucia Zheng, Neel Guha, Javokhir Arifov, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2025-reasoning-focused-legal-retrieval-benchmark/)
1019. [**AI Agents That Matter arXiv:2407.01502**Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, et al.*2024 · 55 citations*](https://md.research.elamur.ai/free/en/papers/kapoor-2024-ai-agents-that-matter/)
1020. **[Language Models Learn Rare Phenomena from Less Rare Phenomena: The Case of the Missing AANNs](https://md.research.elamur.ai/free/en/papers/misra-2024-language-models-learn-rare-phenomena/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kanishka Misra, Kyle Mahowald*2024 · 55 citations*
1021. **[A Chain-of-Thought Is as Strong as Its Weakest Link: A Benchmark for Verifiers of Reasoning Chains](https://md.research.elamur.ai/free/en/papers/jacovi-2024-chain-thought-is-as-strong/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alon Jacovi, Yonatan Bitton, Bernd Bohnet, et al.*2024 · 54 citations*
1022. [**In-context Learning with Retrieved Demonstrations for Language Models: A Survey arXiv:2401.11624**Man Luo, Xin Xu, Yue Liu, et al.*2024 · 54 citations*](https://md.research.elamur.ai/free/en/papers/luo-2024-context-learning-retrieved-demonstrations-language/)
1023. [**CMB: A Comprehensive Medical Benchmark in Chinese arXiv:2308.08833**Xidong Wang, Guiming Hardy Chen, Dingjie Song, et al.*2023 · 54 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-cmb-comprehensive-medical-benchmark/)
1024. [**Harnessing the linguistic signal to predict scalar inferences**Sebastian Schuster, Yuxing Chen, Judith Degen*2020 · 54 citations*](https://md.research.elamur.ai/free/en/papers/schuster-2020-harnessing-linguistic-signal-predict-scalar/)
1025. **[Representing Social Media Users for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/kolchinski-2018-representing-social-media-users-sarcasm/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Y. Alex Kolchinski, Christopher Potts*2018 · 54 citations*
1026. **[Why do language models perform worse for morphologically complex languages?](https://md.research.elamur.ai/free/en/papers/arnett-2025-why-do-language-models-perform/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Catherine Arnett, Benjamin Bergen*2025 · 53 citations*
1027. [**Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models — A Survey arXiv:2404.01869**Philipp Mondorf, Barbara Plank*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/mondorf-2024-accuracy-evaluating-reasoning-behavior-large/)
1028. [**Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? arXiv:2406.13121**Jinhyuk Lee, Anthony Chen, Zhuyun Dai, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-long-context-language-models-subsume/)
1029. [**RM-Bench: Benchmarking Reward Models of Language Models with Subtlety and Style arXiv:2410.16184**Yantao Liu, Zijun Yao, Rui Min, et al.*2024 · 53 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-rm-bench-benchmarking-reward-models/)
1030. [**Language acquisition: do children and language models follow similar learning stages? arXiv:2306.03586**Linnea Evanson, Yair Lakretz, Jean-Rémi King*2023 · 53 citations*](https://md.research.elamur.ai/free/en/papers/evanson-2023-language-acquisition-children-language-models/)
1031. **[The Learnability of In-Context Learning](https://md.research.elamur.ai/free/en/papers/wies-2023-learnability-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Noam Wies, Yoav Levine, Amnon Shashua*2023 · 53 citations*
1032. **[The Alternative Annotator Test for LLM-as-a-Judge: How to Statistically Justify Replacing Human Annotators with LLMs](https://md.research.elamur.ai/free/en/papers/calderon-2025-alternative-annotator-test-llm-as/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nitay Calderon, Roi Reichart, Rotem Dror*2025 · 52 citations*
1033. [**Large Language Models are In-Context Semantic Reasoners rather than Symbolic Reasoners arXiv:2305.14825**Xiaojuan Tang, Zilong Zheng, Jiaqi Li, et al.*2023 · 52 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-large-language-models-context-semantic/)
1034. [**Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics arXiv:2410.21272**Yaniv Nikankin, Anja Reusch, Aaron Mueller, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/nikankin-2024-arithmetic-algorithms-language-models-solve/)
1035. [**From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models arXiv:2406.16838**Sean Welleck, Amanda Bertsch, Matthew Finlayson, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/)
1036. **[VariErr NLI: Separating Annotation Error from Human Label Variation](https://md.research.elamur.ai/free/en/papers/genzel-2024-varierr-nli-separating-annotation-error/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Leon Weber-Genzel, Siyao Peng, Marie-Catherine De Marneffe, et al.*2024 · 51 citations*
1037. [**On the application of Large Language Models for language teaching and assessment technology arXiv:2307.08393**Andrew Caines, Luca Benedetto, Shiva Taslimipoor, et al.*2023 · 51 citations*](https://md.research.elamur.ai/free/en/papers/caines-2023-application-large-language-models-language/)
1038. **[ConjNLI: Natural Language Inference Over Conjunctive Sentences](https://md.research.elamur.ai/free/en/papers/saha-2020-conjnli-natural-language-inference-over/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Swarnadeep Saha, Yixin Nie, Mohit Bansal*2020 · 51 citations*
1039. **[Counterfactually-Augmented SNLI Training Data Does Not Yield Better Generalization Than Unaugmented Data](https://md.research.elamur.ai/free/en/papers/huang-2020-counterfactually-augmented-snli-training-data/) [Workshop on Insights from Negative Results in NLP](https://md.research.elamur.ai/free/en/venues/insights/)**William Huang, Haokun Liu, Samuel R. Bowman*2020 · 51 citations*
1040. **[Unpacking DPO and PPO: Disentangling Best Practices for Learning from Preference Feedback](https://md.research.elamur.ai/free/en/papers/ivison-2024-unpacking-dpo-ppo-disentangling-best/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hamish Ivison, Yizhong Wang, Jiacheng Liu, et al.*2024 · 50 citations*
1041. **[Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/ma-2025-pragmatics-era-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bolei Ma, Yuting Li, Wei Zhou, et al.*2025 · 49 citations*
1042. **[The Effectiveness of LLMs as Annotators: A Comparative Overview and Empirical Analysis of Direct Representation](https://md.research.elamur.ai/free/en/papers/pavlovic-2024-effectiveness-llms-annotators-comparative-overview/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Maja Pavlovic, Massimo Poesio*2024 · 49 citations*
1043. **[Word Embeddings Are Steers for Language Models](https://md.research.elamur.ai/free/en/papers/han-2024-word-embeddings-are-steers-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chi Han, Jialiang Xu, Manling Li, et al.*2024 · 49 citations*
1044. **[From Word Types to Tokens and Back: A Survey of Approaches to Word Meaning Representation and Interpretation](https://md.research.elamur.ai/free/en/papers/apidianaki-2023-word-types-tokens-back-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Marianna Apidianaki*2023 · 49 citations*
1045. **[Verum focus is verum, not focus: Cross-linguistic evidence](https://md.research.elamur.ai/free/en/papers/gutzmann-2020-verum-focus-is-verum-not/) [Glossa a journal of general linguistics](https://md.research.elamur.ai/free/en/venues/glossa-a-journal-of-general-linguistics/)**Daniel Gutzmann, Katharina Hartmann, Lisa Matthewson*2020 · 49 citations*
1046. [**Annotation Guidelines for Questions under Discussion and Information Structure**Arndt Riester, Lisa Brunetti, Kordula De Kuthy*2018 · 49 citations*](https://md.research.elamur.ai/free/en/papers/riester-2018-annotation-guidelines-questions-under/)
1047. **[Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning](https://md.research.elamur.ai/free/en/papers/guo-2018-implicit-discourse-relation-recognition-neural/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Fengyu Guo, Ruifang He, Di Jin, et al.*2018 · 49 citations*
1048. [**Universal Dependencies Parsing for Colloquial Singaporean English**Hongmin Wang, Yue Zhang, GuangYong Leonard Chan, et al.*2017 · 49 citations*](https://md.research.elamur.ai/free/en/papers/wang-2017-universal-dependencies-parsing-colloquial-singaporean/)
1049. [**Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy arXiv:2507.01352**Chris Yuhao Liu, Liang Zeng, Yuzhen Xiao, et al.*2025 · 48 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-skywork-reward-v2-scaling/)
1050. **[Puzzle Solving using Reasoning of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/giadikiaroglou-2024-puzzle-solving-reasoning-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Panagiotis Giadikiaroglou, Maria Lymperaiou, Giorgos Filandrianos, et al.*2024 · 48 citations*
1051. [**Linguistic ambiguity analysis in ChatGPT arXiv:2302.06426**Miguel Ortega-Martin, Oscar Garcia-Sierra, Alfonso Ardoiz, et al.*2023 · 48 citations*](https://md.research.elamur.ai/free/en/papers/ortega-martin-2023-linguistic-ambiguity-analysis-chatgpt/)
1052. **[ANLIzing the Adversarial Natural Language Inference Dataset](https://md.research.elamur.ai/free/en/papers/williams-2022-anlizing-adversarial-natural-language-inference/) [Society for Computation in Linguistics (SCiL)](https://md.research.elamur.ai/free/en/venues/scil/)**Adina Williams, Tristan Thrush, Douwe Kiela*2022 · 48 citations*
1053. [**A Neural Entity Coreference Resolution Review Expert Systems with Applications, Volume 168, 15 April 2021, 114,466**Nikolaos Stylianou, Ioannis Vlahavas*2019 · 48 citations*](https://md.research.elamur.ai/free/en/papers/stylianou-2019-neural-entity-coreference-resolution-review/)
1054. **[Can Multiple-choice Questions Really Be Useful in Detecting the Abilities of LLMs?](https://md.research.elamur.ai/free/en/papers/li-2024-can-multiple-choice-questions-really/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Wangyue Li, Liangzhi Li, Tong Xiang, et al.*2024 · 47 citations*
1055. **[Metaphor Understanding Challenge Dataset for LLMs](https://md.research.elamur.ai/free/en/papers/tong-2024-metaphor-understanding-challenge-dataset-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.*2024 · 47 citations*
1056. [**Language models scale reliably with over-training and on downstream tasks arXiv:2403.08540**Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/gadre-2024-language-models-scale-reliably-over/)
1057. [**Stable LM 2 1.6B Technical Report arXiv:2402.17834**Marco Bellagente, Jonathan Tow, Dakota Mahan, et al.*2024 · 46 citations*](https://md.research.elamur.ai/free/en/papers/bellagente-2024-stable-lm-2-1-6b/)
1058. **[“According to . . . “: Prompting Language Models Improves Quoting from Pre-Training Data](https://md.research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Orion Weller, Marc Marone, Nathaniel Weir, et al.*2024 · 46 citations*
1059. **[Cross-Lingual Knowledge Editing in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-cross-lingual-knowledge-editing-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiaan Wang, Yunlong Liang, Zengkui Sun, et al.*2024 · 45 citations*
1060. [**GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements arXiv:2402.10963**Alex Havrilla, Sharath Raparthy, Christoforus Nalmpantis, et al.*2024 · 45 citations*](https://md.research.elamur.ai/free/en/papers/havrilla-2024-glore-where-improve-llm-reasoning/)
1061. [**Lost in Translation: Large Language Models in Non-English Content Analysis arXiv:2306.07377**Gabriel Nicholas, Aliya Bhatia*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/nicholas-2023-lost-translation-large-language-models/)
1062. **[Implicatures and Nested Beliefs in Approximate Decentralized-POMDPs](https://md.research.elamur.ai/free/en/papers/vogel-2013-implicatures-nested-beliefs-approximate-decentralized/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Adam Vogel, Christopher Potts, Dan Jurafsky*2013 · 45 citations*
1063. [**HyperCLOVA X Technical Report arXiv:2404.01954**Kang Min Yoo, Jaegeun Han, Sookyo In, et al.*2024 · 44 citations*](https://md.research.elamur.ai/free/en/papers/yoo-2024-hyperclova-x-technical-report/)
1064. **[Long-Context Language Modeling with Parallel Context Encoding](https://md.research.elamur.ai/free/en/papers/yen-2024-long-context-language-modeling-parallel/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Howard Yen, Tianyu Gao, Danqi Chen*2024 · 44 citations*
1065. **[Polysemy–-Evidence from Linguistics, Behavioral Science, and Contextualized Language Models](https://md.research.elamur.ai/free/en/papers/haber-2024-polysemy-evidence-linguistics-behavioral-science/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Janosch Haber, Massimo Poesio*2024 · 44 citations*
1066. [**SQUINKY! A Corpus of Sentence-level Formality, Informativeness, and Implicature arXiv:1506.02306**Shibamouli Lahiri*2015 · 44 citations*](https://md.research.elamur.ai/free/en/papers/lahiri-2015-squinky-corpus-sentence-level-formality/)
1067. **[Information Flow Routes: Automatically Interpreting Language Models at Scale](https://md.research.elamur.ai/free/en/papers/ferrando-2024-information-flow-routes-automatically-interpreting/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Javier Ferrando, Elena Voita*2024 · 43 citations*
1068. [**Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models arXiv:2308.11764**Mohamed Elaraby, Mengyin Lu, Jacob Dunn, et al.*2023 · 43 citations*](https://md.research.elamur.ai/free/en/papers/elaraby-2023-halo-estimation-reduction-hallucinations-open/)
1069. **[SciNLI: A Corpus for Natural Language Inference on Scientific Text](https://md.research.elamur.ai/free/en/papers/sadat-2022-scinli-corpus-natural-language-inference/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mobashir Sadat, Cornelia Caragea*2022 · 43 citations*
1070. [**A Mention-Ranking Model for Abstract Anaphora Resolution**Ana Marasović, Leo Born, Juri Opitz, et al.*2017 · 43 citations*](https://md.research.elamur.ai/free/en/papers/marasovic-2017-mention-ranking-model-abstract-anaphora/)
1071. [**SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models arXiv:2311.08370**Bertie Vidgen, Nino Scherrer, Hannah Rose Kirk, et al.*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2023-simplesafetytests-test-suite-identifying-critical/)
1072. [**When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks arXiv:2311.08993**Hao Peng, Xiaozhi Wang, Jianhui Chen, et al.*2023 · 42 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-context-learning-fall-short-why/)
1073. [**Transcending Scaling Laws with 0.1% Extra Compute arXiv:2210.11399**Yi Tay, Jason Wei, Hyung Won Chung, et al.*2022 · 42 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-transcending-scaling-laws-0-1/)
1074. **[Syntax-augmented Multilingual BERT for Cross-lingual Transfer](https://md.research.elamur.ai/free/en/papers/ahmad-2021-syntax-augmented-multilingual-bert-cross/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wasi Ahmad, Haoran Li, Kai-Wei Chang, et al.*2021 · 42 citations*
1075. [**Auxiliary task demands mask the capabilities of smaller language models arXiv:2404.02418**Jennifer Hu, Michael C. Frank*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-auxiliary-task-demands-mask-capabilities/)
1076. **[MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks](https://md.research.elamur.ai/free/en/papers/nie-2023-moca-measuring-human-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Allen Nie, Yuhui Zhang, Atharva Amdekar, et al.*2023 · 41 citations*
1077. [**SALMON: Self-Alignment with Instructable Reward Models arXiv:2310.05910**Zhiqing Sun, Yikang Shen, Hongxin Zhang, et al.*2023 · 41 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-salmon-self-alignment-instructable-reward/)
1078. **[How Can Self-Attention Networks Recognize Dyck-n Languages?](https://md.research.elamur.ai/free/en/papers/ebrahimi-2020-how-can-self-attention-networks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Javid Ebrahimi, Dhruv Gelda, Wei Zhang*2020 · 41 citations*
1079. **[Character-Aware Neural Networks for Arabic Named Entity Recognition for Social Media](https://md.research.elamur.ai/free/en/papers/gridach-2016-character-aware-neural-networks-arabic/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Mourad Gridach*2016 · 41 citations*
1080. **[A Systematic Survey of Automatic Prompt Optimization Techniques](https://md.research.elamur.ai/free/en/papers/ramnath-2025-systematic-survey-automatic-prompt-optimization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kiran Ramnath, Kang Zhou, Sheng Guan, et al.*2025 · 40 citations*
1081. **[Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?](https://md.research.elamur.ai/free/en/papers/yona-2024-can-large-language-models-faithfully/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gal Yona, Roee Aharoni, Mor Geva*2024 · 40 citations*
1082. [**Observational Scaling Laws and the Predictability of Language Model Performance arXiv:2405.10938**Yangjun Ruan, Chris J. Maddison, Tatsunori Hashimoto*2024 · 40 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-observational-scaling-laws-predictability-language/)
1083. [**NOPE: A Corpus of Naturally-Occurring Presuppositions in English**Alicia Parrish, Sebastian Schuster, Alex Warstadt, et al.*2021 · 40 citations*](https://md.research.elamur.ai/free/en/papers/parrish-2021-nope-corpus-naturally-occurring-presuppositions/)
1084. **[Frustrated, Polite, or Formal: Quantifying Feelings and Tone in Email](https://md.research.elamur.ai/free/en/papers/chhaya-2018-frustrated-polite-or-formal-quantifying/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Niyati Chhaya, Kushal Chawla, Tanya Goyal, et al.*2018 · 40 citations*
1085. **[Exploring Joint Neural Model for Sentence Level Discourse Parsing and Sentiment Analysis](https://md.research.elamur.ai/free/en/papers/nejat-2017-exploring-joint-neural-model-sentence/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Bita Nejat, Giuseppe Carenini, Raymond Ng*2017 · 40 citations*
1086. **[Weasels, Hedges and Peacocks: Discourse-level Uncertainty in Wikipedia Articles](https://md.research.elamur.ai/free/en/papers/vincze-2013-weasels-hedges-peacocks-discourse-level/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Veronika Vincze*2013 · 40 citations*
1087. **[Learning to Edit: Aligning LLMs with Knowledge Editing](https://md.research.elamur.ai/free/en/papers/jiang-2024-learning-edit-aligning-llms-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxin Jiang, Yufei Wang, Chuhan Wu, et al.*2024 · 39 citations*
1088. [**Aligning Language Models with Preferences through f-divergence Minimization arXiv:2302.08215**Dongyoung Go, Tomasz Korbak, German Kruszewski, et al.*2023 · 39 citations*](https://md.research.elamur.ai/free/en/papers/go-2023-aligning-language-models-preferences-through/)
1089. [**Facilitating the Communication of Politeness through Fine-Grained Paraphrasing**Liye Fu, Susan Fussell, Cristian Danescu-Niculescu-Mizil*2020 · 39 citations*](https://md.research.elamur.ai/free/en/papers/fu-2020-facilitating-communication-politeness-through-fine/)
1090. [**Arc-swift: A Novel Transition System for Dependency Parsing**Peng Qi, Christopher D. Manning*2017 · 39 citations*](https://md.research.elamur.ai/free/en/papers/qi-2017-arc-swift-novel-transition-system/)
1091. **[Natural Language Inference from Multiple Premises](https://md.research.elamur.ai/free/en/papers/lai-2017-natural-language-inference-multiple-premises/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Alice Lai, Yonatan Bisk, Julia Hockenmaier*2017 · 39 citations*
1092. **[CulturalBench: A Robust, Diverse and Challenging Benchmark for Measuring LMs' Cultural Knowledge Through Human-AI Red-Teaming](https://md.research.elamur.ai/free/en/papers/chiu-2025-culturalbench-robust-diverse-challenging-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yu Ying Chiu, Liwei Jiang, Bill Yuchen Lin, et al.*2025 · 38 citations*
1093. **[CausalGym: Benchmarking causal interpretability methods on linguistic tasks](https://md.research.elamur.ai/free/en/papers/arora-2024-causalgym-benchmarking-causal-interpretability-methods/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Aryaman Arora, Dan Jurafsky, Christopher Potts*2024 · 38 citations*
1094. [**Introducing v0.5 of the AI Safety Benchmark from MLCommons arXiv:2404.12241**Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.*2024 · 38 citations*](https://md.research.elamur.ai/free/en/papers/vidgen-2024-introducing-v0-5-ai-safety/)
1095. **[JASMINE: Arabic GPT Models for Few-Shot Learning](https://md.research.elamur.ai/free/en/papers/nagoudi-2023-jasmine-arabic-gpt-models-few/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**El Moatez Billah Nagoudi, Muhammad Abdul-Mageed, AbdelRahim Elmadany, et al.*2023 · 38 citations*
1096. **[Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://md.research.elamur.ai/free/en/papers/fried-2023-pragmatics-language-grounding-phenomena-tasks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Daniel Fried, Nicholas Tomlin, Jennifer Hu, et al.*2023 · 38 citations*
1097. **[Identifying inherent disagreement in natural language inference](https://md.research.elamur.ai/free/en/papers/zhang-2021-identifying-inherent-disagreement-natural-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xinliang Frederick Zhang, Marie-Catherine de Marneffe*2021 · 38 citations*
1098. **[Continual Adaptation for Efficient Machine Communication](https://md.research.elamur.ai/free/en/papers/hawkins-2020-continual-adaptation-efficient-machine-communication/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Robert Hawkins, Minae Kwon, Dorsa Sadigh, et al.*2020 · 38 citations*
1099. **[OG-RAG: Ontology-grounded retrieval-augmented generation for large language models](https://md.research.elamur.ai/free/en/papers/sharma-2025-og-rag-ontology-grounded-retrieval/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kartik Sharma, Peeyush Kumar, Yunqing Li*2025 · 37 citations*
1100. **[Aligning Large Language Models with Human Preferences through Representation Engineering](https://md.research.elamur.ai/free/en/papers/liu-2024-aligning-large-language-models-human/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenhao Liu, Xiaohua Wang, Muling Wu, et al.*2024 · 37 citations*

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