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title: "Research Triangle — works: 120 in our library, the most cited first"
description: "Works of Research Triangle: 120 in our library · page 1 of 2, the most cited first. Among them: Stop Explaining Black Box Machine Learning…"
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# Works of Research Triangle

120 · page 1 of 2 · the most cited first · [← Research Triangle](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/)

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1. **[Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/) [Nature Machine Intelligence](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/)**Cynthia Rudin*2018 · 10,460 citations*
2. **[Statsmodels: econometric and statistical modeling with Python](https://md.research.elamur.ai/free/en/papers/seabold-2010-statsmodels-econometric-statistical-modeling-python/) [Proceedings of the Python in Science Conferences](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-python-in-science-conferences/)**Skipper Seabold, Josef Perktold*2010 · 6,540 citations*
3. **[LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://md.research.elamur.ai/free/en/papers/tan-2019-lxmert-learning-cross-modality-encoder/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hao Tan, Mohit Bansal*2019 · 2,789 citations*
4. [**Emergent Abilities of Large Language Models arXiv:2206.07682**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,269 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
5. **[ReferItGame: Referring to Objects in Photographs of Natural Scenes](https://md.research.elamur.ai/free/en/papers/kazemzadeh-2014-referitgame-referring-objects-photographs-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, et al.*2014 · 1,317 citations*
6. **[Adversarial NLI: A New Benchmark for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/nie-2020-adversarial-nli-new-benchmark-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Nie, Adina Williams, Emily Dinan, et al.*2020 · 1,296 citations*
7. [**What Makes Good In-Context Examples for GPT-3? Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures**Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al.*2022 · 1,256 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/)
8. **[Modeling Context in Referring Expressions](https://md.research.elamur.ai/free/en/papers/yu-2016-modeling-context-referring-expressions/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Licheng Yu, Patrick Poirson, Shan Yang, et al.*2016 · 1,203 citations*
9. **[Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://md.research.elamur.ai/free/en/papers/chen-2018-fast-abstractive-summarization-reinforce-selected/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yen-Chun Chen, Mohit Bansal*2018 · 826 citations*
10. **[A Novel Cascade Binary Tagging Framework for Relational Triple Extraction](https://md.research.elamur.ai/free/en/papers/wei-2020-novel-cascade-binary-tagging-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhepei Wei, Jianlin Su, Yue Wang, et al.*2020 · 736 citations*
11. **[Entity-Based Cross-Document Coreferencing Using the Vector Space Model](https://md.research.elamur.ai/free/en/papers/bagga-1998-entity-based-cross-document-coreferencing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Amit Bagga, Breck Baldwin*1998 · 701 citations*
12. **[TVQA: Localized, Compositional Video Question Answering](https://md.research.elamur.ai/free/en/papers/lei-2018-tvqa-localized-compositional-video-question/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jie Lei, Licheng Yu, Mohit Bansal, et al.*2018 · 687 citations*
13. **[Joint Embedding of Words and Labels for Text Classification](https://md.research.elamur.ai/free/en/papers/wang-2018-joint-embedding-words-labels-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guoyin Wang, Chunyuan Li, Wenlin Wang, et al.*2018 · 465 citations*
14. **[Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms](https://md.research.elamur.ai/free/en/papers/shen-2018-baseline-needs-more-love-simple/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dinghan Shen, Guoyin Wang, Wenlin Wang, et al.*2018 · 386 citations*
15. **[Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://md.research.elamur.ai/free/en/papers/tan-2019-learning-navigate-unseen-environments-back/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hao Tan, Licheng Yu, Mohit Bansal*2019 · 358 citations*
16. **[Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?](https://md.research.elamur.ai/free/en/papers/hase-2020-evaluating-explainable-ai-which-algorithmic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peter Hase, Mohit Bansal*2020 · 318 citations*
17. [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts arXiv:2306.04528**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/)
18. **[TreeTalk: Composition and Compression of Trees for Image Descriptions](https://md.research.elamur.ai/free/en/papers/kuznetsova-2014-treetalk-composition-compression-trees-image/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Polina Kuznetsova, Vicente Ordonez, Tamara L. Berg, et al.*2014 · 290 citations*
19. **[Contextualized Perturbation for Textual Adversarial Attack](https://md.research.elamur.ai/free/en/papers/li-2021-contextualized-perturbation-textual-adversarial-attack/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Dianqi Li, Yizhe Zhang, Hao Peng, et al.*2021 · 283 citations*
20. [**Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning OSDI 2022**Lianmin Zheng, Zhuohan Li, Hao Zhang, et al.*2022 · 279 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2022-alpa-automating-inter-intra-operator/)
21. **[Humanity's Last Exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 256 citations*
22. **[Multi-Reward Reinforced Summarization with Saliency and Entailment](https://md.research.elamur.ai/free/en/papers/pasunuru-2018-multi-reward-reinforced-summarization-saliency/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ramakanth Pasunuru, Mohit Bansal*2018 · 255 citations*
23. **[An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://md.research.elamur.ai/free/en/papers/chen-2023-empirical-survey-data-augmentation-limited/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jiaao Chen, Derek Tam, Colin Raffel, et al.*2023 · 254 citations*
24. **[ASQA: Factoid Questions Meet Long-Form Answers](https://md.research.elamur.ai/free/en/papers/stelmakh-2022-asqa-factoid-questions-meet-long/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, et al.*2022 · 245 citations*
25. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
26. **[Commonsense for Generative Multi-Hop Question Answering Tasks](https://md.research.elamur.ai/free/en/papers/bauer-2018-commonsense-generative-multi-hop-question/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lisa Bauer, Yicheng Wang, Mohit Bansal*2018 · 229 citations*
27. **[HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification](https://md.research.elamur.ai/free/en/papers/jiang-2020-hover-dataset-many-hop-fact/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yichen Jiang, Shikha Bordia, Zheng Zhong, et al.*2020 · 228 citations*
28. **[Merging Models with Fisher-Weighted Averaging](https://md.research.elamur.ai/free/en/papers/matena-2021-merging-models-fisher-weighted-averaging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Matena, Colin Raffel*2021 · 208 citations*
29. **[What Can We Learn from Collective Human Opinions on Natural Language Inference Data?](https://md.research.elamur.ai/free/en/papers/nie-2020-what-can-we-learn-collective/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yixin Nie, Xiang Zhou, Mohit Bansal*2020 · 196 citations*
30. **[Revealing the Importance of Semantic Retrieval for Machine Reading at Scale](https://md.research.elamur.ai/free/en/papers/nie-2019-revealing-importance-semantic-retrieval-machine/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yixin Nie, Songhe Wang, Mohit Bansal*2019 · 190 citations*
31. **[Improving and Simplifying Pattern Exploiting Training](https://md.research.elamur.ai/free/en/papers/tam-2021-improving-simplifying-pattern-exploiting-training/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Derek Tam, Rakesh R. Menon, Mohit Bansal, et al.*2021 · 189 citations*
32. **[GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models](https://md.research.elamur.ai/free/en/papers/prasad-2023-grips-gradient-free-edit-based/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Archiki Prasad, Peter Hase, Xiang Zhou, et al.*2023 · 184 citations*
33. [**BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions arXiv:2406.15877**Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, et al.*2024 · 176 citations*](https://md.research.elamur.ai/free/en/papers/zhuo-2024-bigcodebench-benchmarking-code-generation-diverse/)
34. **[Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering](https://md.research.elamur.ai/free/en/papers/zhang-2019-addressing-semantic-drift-question-generation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shiyue Zhang, Mohit Bansal*2019 · 174 citations*
35. **[ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs](https://md.research.elamur.ai/free/en/papers/chen-2024-reconcile-round-table-conference-improves/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Justin Chen, Swarnadeep Saha, Mohit Bansal*2024 · 156 citations*
36. **[Efficient Methods for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/treviso-2023-efficient-methods-natural-language-processing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marcos Treviso, Ji-Ung Lee, Tianchu Ji, et al.*2023 · 145 citations*
37. **[Avoiding Reasoning Shortcuts: Adversarial Evaluation, Training, and Model Development for Multi-Hop QA](https://md.research.elamur.ai/free/en/papers/jiang-2019-avoiding-reasoning-shortcuts-adversarial-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yichen Jiang, Mohit Bansal*2019 · 143 citations*
38. [**Measuring the Intrinsic Dimension of Objective Landscapes arXiv:1804.08838**Chunyuan Li, Heerad Farkhoor, Rosanne Liu, et al.*2018 · 143 citations*](https://md.research.elamur.ai/free/en/papers/li-2018-measuring-intrinsic-dimension-objective-landscapes/)
39. **[SummScreen: A Dataset for Abstractive Screenplay Summarization](https://md.research.elamur.ai/free/en/papers/chen-2022-summscreen-dataset-abstractive-screenplay-summarization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mingda Chen, Zewei Chu, Sam Wiseman, et al.*2022 · 136 citations*
40. [**Analyzing and Mitigating Object Hallucination in Large Vision-Language Models arXiv:2310.00754**Yiyang Zhou, Chenhang Cui, Jaehong Yoon, et al.*2023 · 134 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-analyzing-mitigating-object-hallucination-large/)
41. [**Fine-tuning Language Models for Factuality arXiv:2311.08401**Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/)
42. **[Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?](https://md.research.elamur.ai/free/en/papers/hase-2020-leakage-adjusted-simulatability-can-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Peter Hase, Shiyue Zhang, Harry Xie, et al.*2020 · 126 citations*
43. **[Analyzing Compositionality-Sensitivity of NLI Models](https://md.research.elamur.ai/free/en/papers/nie-2018-analyzing-compositionality-sensitivity-nli-models/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yixin Nie, Yicheng Wang, Mohit Bansal*2018 · 121 citations*
44. [**Mixture-of-Agents Enhances Large Language Model Capabilities arXiv:2406.04692**Junlin Wang, Jue Wang, Ben Athiwaratkun, et al.*2024 · 116 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-mixture-agents-enhances-large/)
45. **[PRover: Proof Generation for Interpretable Reasoning over Rules](https://md.research.elamur.ai/free/en/papers/saha-2020-prover-proof-generation-interpretable-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, et al.*2020 · 103 citations*
46. **[FactPEGASUS: Factuality-Aware Pre-training and Fine-tuning for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/wan-2022-factpegasus-factuality-aware-pre-training/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**David Wan, Mohit Bansal*2022 · 101 citations*
47. [**Large Language Models are Inconsistent and Biased Evaluators arXiv:2405.01724**Rickard Stureborg, Dimitris Alikaniotis, Yoshi Suhara*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/stureborg-2024-large-language-models-inconsistent-biased/)
48. **[ADaPT: As-Needed Decomposition and Planning with Language Models](https://md.research.elamur.ai/free/en/papers/prasad-2024-adapt-as-needed-decomposition-planning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Archiki Prasad, Alexander Koller, Mareike Hartmann, et al.*2024 · 89 citations*
49. **[Selectively Answering Ambiguous Questions](https://md.research.elamur.ai/free/en/papers/cole-2023-selectively-answering-ambiguous-questions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jeremy Cole, Michael Zhang, Daniel Gillick, et al.*2023 · 84 citations*
50. [**Formalizing and Benchmarking Prompt Injection Attacks and Defenses arXiv:2310.12815**Yupei Liu, Yuqi Jia, Runpeng Geng, et al.*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-formalizing-benchmarking-prompt-injection-attacks/)
51. [**Punica: Multi-Tenant LoRA Serving MLSys 2024**Lequn Chen, Zihao Ye, Yongji Wu, et al.*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-punica-multi-tenant-lora-serving/)
52. [**MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use arXiv:2310.03128**Yue Huang, Jiawen Shi, Yuan Li, et al.*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-metatool-benchmark-large-language-models/)
53. **[ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness](https://md.research.elamur.ai/free/en/papers/prasad-2023-receval-evaluating-reasoning-chains-correctness/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Archiki Prasad, Swarnadeep Saha, Xiang Zhou, et al.*2023 · 73 citations*
54. **[Towards Robustifying NLI Models Against Lexical Dataset Biases](https://md.research.elamur.ai/free/en/papers/zhou-2020-robustifying-nli-models-against-lexical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiang Zhou, Mohit Bansal*2020 · 70 citations*
55. [**Branch-Solve-Merge Improves Large Language Model Evaluation and Generation arXiv:2310.15123**Swarnadeep Saha, Omer Levy, Asli Celikyilmaz, et al.*2023 · 68 citations*](https://md.research.elamur.ai/free/en/papers/saha-2023-branch-solve-merge-improves-large/)
56. [**OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs arXiv:2411.14199**Akari Asai, Jacqueline He, Rulin Shao, et al.*2024 · 62 citations*](https://md.research.elamur.ai/free/en/papers/asai-2024-openscholar-synthesizing-scientific-literature/)
57. [**On Generalization in Coreference Resolution**Shubham Toshniwal, Patrick Xia, Sam Wiseman, et al.*2021 · 62 citations*](https://md.research.elamur.ai/free/en/papers/toshniwal-2021-generalization-coreference-resolution/)
58. **[An Affect-Enriched Dialogue Act Classification Model for Task-Oriented Dialogue](https://md.research.elamur.ai/free/en/papers/boyer-2011-affect-enriched-dialogue-act-classification/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kristy Boyer, Joseph Grafsgaard, Eun Young Ha, et al.*2011 · 60 citations*
59. **[Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models](https://md.research.elamur.ai/free/en/papers/hase-2023-methods-measuring-updating-visualizing-factual/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Peter Hase, Mona Diab, Asli Celikyilmaz, et al.*2023 · 51 citations*
60. **[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*
61. **[Dialogue Act Modeling in a Complex Task-Oriented Domain](https://md.research.elamur.ai/free/en/papers/boyer-2010-dialogue-act-modeling-complex-task/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Kristy Boyer, Eun Y. Ha, Robert Phillips, et al.*2010 · 42 citations*
62. **[A Transformer Approach to Contextual Sarcasm Detection in Twitter](https://md.research.elamur.ai/free/en/papers/gregory-2020-transformer-approach-contextual-sarcasm-detection/) [Workshop on Figurative Language Processing](https://md.research.elamur.ai/free/en/venues/figlang/)**Hunter Gregory, Steven Li, Pouya Mohammadi, et al.*2020 · 38 citations*
63. **[Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning](https://md.research.elamur.ai/free/en/papers/zhou-2022-distributed-nli-learning-predict-human/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiang Zhou, Yixin Nie, Mohit Bansal*2022 · 36 citations*
64. **[Seq2seq is All You Need for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/zhang-2023-seq2seq-is-all-you-need/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenzheng Zhang, Sam Wiseman, Karl Stratos*2023 · 32 citations*
65. **[Contextualization Distillation from Large Language Model for Knowledge Graph Completion](https://md.research.elamur.ai/free/en/papers/li-2024-contextualization-distillation-large-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dawei Li, Zhen Tan, Tianlong Chen, et al.*2024 · 30 citations*
66. [**RMB: Comprehensively Benchmarking Reward Models in LLM Alignment arXiv:2410.09893**Enyu Zhou, Guodong Zheng, Binghai Wang, et al.*2024 · 30 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-rmb-comprehensively-benchmarking-reward-models/)
67. **[The Role of Sentence Structure in Recognizing Textual Entailment](https://md.research.elamur.ai/free/en/papers/blake-2007-role-sentence-structure-recognizing-textual/) [ACL-PASCAL Workshop on Textual Entailment and Paraphrasing](https://md.research.elamur.ai/free/en/venues/acl-pascal-workshop-on-textual-entailment-and-paraphrasing/)**Catherine Blake*2007 · 20 citations*
68. **[MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning](https://md.research.elamur.ai/free/en/papers/chen-2025-magicore-multi-agent-iterative-coarse/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Justin Chen, Archiki Prasad, Swarnadeep Saha, et al.*2025 · 20 citations*
69. **[Multilingual and Explainable Text Detoxification with Parallel Corpora](https://md.research.elamur.ai/free/en/papers/dementieva-2025-multilingual-explainable-text-detoxification-parallel/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Daryna Dementieva, Nikolay Babakov, Amit Ronen, et al.*2025 · 19 citations*
70. **[Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs](https://md.research.elamur.ai/free/en/papers/xia-2025-beyond-chain-thought-survey-chain/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Yu Xia, Rui Wang, Xu Liu, et al.*2025 · 19 citations*
71. **[Sound change and coarticulatory variability involving English /ɹ/](https://md.research.elamur.ai/free/en/papers/smith-2019-sound-change-coarticulatory-variability-involving/) [Glossa a journal of general linguistics](https://md.research.elamur.ai/free/en/venues/glossa-a-journal-of-general-linguistics/)**Bridget J. Smith, Jeff Mielke, Lyra Magloughlin, et al.*2019 · 18 citations*
72. **[Does Commonsense help in detecting Sarcasm?](https://md.research.elamur.ai/free/en/papers/chowdhury-2021-does-commonsense-help-detecting-sarcasm/) [Workshop on Insights from Negative Results in NLP](https://md.research.elamur.ai/free/en/venues/insights/)**Somnath Basu Roy Chowdhury, Snigdha Chaturvedi*2021 · 17 citations*
73. [**Zero and Few-shot Semantic Parsing with Ambiguous Inputs arXiv:2306.00824**Elias Stengel-Eskin, Kyle Rawlins, Benjamin Van Durme*2023 · 15 citations*](https://md.research.elamur.ai/free/en/papers/stengel-eskin-2023-zero-few-shot-semantic-parsing/)
74. **[Multi-Attribute Steering of Language Models via Targeted Intervention](https://md.research.elamur.ai/free/en/papers/nguyen-2025-multi-attribute-steering-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Duy Nguyen, Archiki Prasad, Elias Stengel-Eskin, et al.*2025 · 11 citations*
75. **[Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond](https://md.research.elamur.ai/free/en/papers/sanni-2025-afrispeech-dialog-benchmark-dataset-spontaneous/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Mardhiyah Sanni, Tassallah Abdullahi, Devendra Deepak Kayande, et al.*2025 · 11 citations*
76. **[Combining Task and Dialogue Streams in Unsupervised Dialogue Act Models](https://md.research.elamur.ai/free/en/papers/can-2014-combining-task-dialogue-streams-unsupervised/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Aysu Ezen-Can, Kristy Boyer*2014 · 10 citations*
77. **[Synthetic Socratic Debates: Examining Persona Effects on Moral Decision and Persuasion Dynamics](https://md.research.elamur.ai/free/en/papers/liu-2025-synthetic-socratic-debates-examining-persona/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiarui Liu, Yueqi Song, Yunze Xiao, et al.*2025 · 9 citations*
78. **[LAQuer: Localized Attribution Queries in Content-grounded Generation](https://md.research.elamur.ai/free/en/papers/hirsch-2025-laquer-localized-attribution-queries-content/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eran Hirsch, Aviv Slobodkin, David Wan, et al.*2025 · 8 citations*
79. **[Localizing Factual Inconsistencies in Attributable Text Generation](https://md.research.elamur.ai/free/en/papers/cattan-2026-localizing-factual-inconsistencies-attributable-text/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Arie Cattan, Paul Roit, Shiyue Zhang, et al.*2026 · 7 citations*
80. **[In-Context Evaluation of Unsupervised Dialogue Act Models for Tutorial Dialogue](https://md.research.elamur.ai/free/en/papers/can-2013-context-evaluation-unsupervised-dialogue-act/) [SIGDIAL Conference](https://md.research.elamur.ai/free/en/venues/sigdial-conference/)**Aysu Ezen-Can, Kristy Boyer*2013 · 6 citations*
81. **[Dual Process Masking for Dialogue Act Recognition](https://md.research.elamur.ai/free/en/papers/kim-2024-dual-process-masking-dialogue-act/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yeo Jin Kim, Halim Acosta, Wookhee Min, et al.*2024 · 6 citations*
82. [**HATFormer: Historic Handwritten Arabic Text Recognition with Transformers arXiv:2410.02179**Adrian Chan, Anupam Mijar, Mehreen Saeed, et al.*2024 · 5 citations*](https://md.research.elamur.ai/free/en/papers/chan-2024-hatformer-historic-handwritten-arabic-text/)
83. **[Embedding-Informed Adaptive Retrieval-Augmented Generation of Large Language Models](https://md.research.elamur.ai/free/en/papers/huang-2025-embedding-informed-adaptive-retrieval-augmented/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Chengkai Huang, Yu Xia, Rui Wang, et al.*2025 · 5 citations*
84. **[Is Chain-of-Thought Really Not Explainability? Chain-of-Thought Can Be Faithful without Hint Verbalization](https://md.research.elamur.ai/free/en/papers/zaman-2026-is-chain-thought-really-not/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kerem Zaman, Shashank Srivastava*2026 · 5 citations*
85. [**Critical biblical studies via word frequency analysis: unveiling text authorship**Shira Faigenbaum-Golovin, Alon Kipnis, Axel Buhler, et al.*2024 · 3 citations*](https://md.research.elamur.ai/free/en/papers/faigenbaum-golovin-2024-critical-biblical-studies-word-frequency/)
86. **[Improving Classroom Dialogue Act Recognition from Limited Labeled Data with Self-Supervised Contrastive Learning Classifiers](https://md.research.elamur.ai/free/en/papers/kumaran-2023-improving-classroom-dialogue-act-recognition/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Vikram Kumaran, Jonathan Rowe, Bradford Mott, et al.*2023 · 3 citations*
87. **[Improving Automated Prediction of English Lexical Blends Through the Use of Observable Linguistic Features](https://md.research.elamur.ai/free/en/papers/saunders-2023-improving-automated-prediction-english-lexical/) [Workshop of the Special Interest Group on Computational Morphology and Phonology (SIGMORPHON)](https://md.research.elamur.ai/free/en/venues/sigmorphon/)**Jarem Saunders*2023 · 3 citations*
88. **[Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction](https://md.research.elamur.ai/free/en/papers/shen-2026-metacognitive-self-correction-multi-agent/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xu Shen, Qi Zhang, Song Wang, et al.*2026 · 3 citations*
89. **[CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data](https://md.research.elamur.ai/free/en/papers/suarez-2026-commonlid-re-evaluating-state-art/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett, et al.*2026 · 3 citations*
90. [**The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation**Edwin Agnew, Michelle Qiu, Lily Zhu, et al.*2023 · 2 citations*](https://md.research.elamur.ai/free/en/papers/agnew-2023-mechanical-bard-interpretable-machine-learning/)
91. **[Pragmatic Reasoning Unlocks Quantifier Semantics for Foundation Models](https://md.research.elamur.ai/free/en/papers/li-2023-pragmatic-reasoning-unlocks-quantifier-semantics/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yiyuan Li, Rakesh Menon, Sayan Ghosh, et al.*2023 · 2 citations*
92. **[RotBench: Evaluating Multi-modal Large Language Models on Identifying Image Rotation](https://md.research.elamur.ai/free/en/papers/niu-2026-rotbench-evaluating-multi-modal-large/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Tianyi Niu, Jaemin Cho, Elias Stengel-Eskin, et al.*2026 · 1 citation*
93. **[DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning](https://md.research.elamur.ai/free/en/papers/sivakumaran-2026-dart-leveraging-multi-agent-disagreement/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Nithin Sivakumaran, Justin Chen, David Wan, et al.*2026 · 1 citation*
94. **[STAMP: Selective Task-Aware Mechanism for Text Privacy](https://md.research.elamur.ai/free/en/papers/tian-2026-stamp-selective-task-aware-mechanism/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Fengwei Tian, Payel Bhattacharjee, Heidi Hanson, et al.*2026 · 1 citation*
95. **[Do LLM Agents Mirror Socio-Cognitive Effects in Power-Asymmetric Conversations?](https://md.research.elamur.ai/free/en/papers/vijjini-2026-do-llm-agents-mirror-socio/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Anvesh Rao Vijjini, Sagar B. Manjunath, Snigdha Chaturvedi*2026 · 1 citation*
96. **[Anchor: Branch-Point Data Generation for GUI Agents](https://md.research.elamur.ai/free/en/papers/wei-2026-anchor-branch-point-data-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinbiao Wei, Yilun Zhao, Kangqi Ni, et al.*2026 · 1 citation*
97. **[SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator](https://md.research.elamur.ai/free/en/papers/zhou-2026-safeagent-safeguarding-llm-agents-automated/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xueyang Zhou, Weidong Wang, Lin Lu, et al.*2026 · 1 citation*
98. **[KGRxn-LLM: Knowledge Graph Enhanced Large Language Models for Molecular Reaction Reasoning](https://md.research.elamur.ai/free/en/papers/liu-2026-kgrxn-llm-knowledge-graph-enhanced/) [BioNLP](https://md.research.elamur.ai/free/en/venues/bionlp/)**Weichen Liu, Qiyao Xue, Yuyang Wu, et al.*2026*
99. **[Learning to Control Summaries with Score Ranking](https://md.research.elamur.ai/free/en/papers/liu-2026-learning-control-summaries-score-ranking/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hongye Liu, Liang Ding, Ricardo Henao*2026*
100. **[The Effects of Structured LLM-Generated Feedback on Programming Assignment Performance](https://md.research.elamur.ai/free/en/papers/mihaylova-2026-effects-structured-llm-generated-feedback/) [Workshop on Innovative Use of NLP for Building Educational Applications (BEA)](https://md.research.elamur.ai/free/en/venues/bea/)**Tsvetomila Mihaylova, Evanfiya Logacheva, Arto Hellas, et al.*2026*

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