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# Works of Findings of the Association for Computational Linguistics

233 works · page 1 of 3 · the most cited first · [← Findings of the Association for Computational…](https://md.research.elamur.ai/free/en/venues/findings/)

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1. [**CodeBERT: A Pre-Trained Model for Programming and Natural Languages**Zhangyin Feng, Daya Guo, Duyu Tang, et al.*2020 · 2,954 citations*](https://md.research.elamur.ai/free/en/papers/feng-2020-codebert-pre-trained-model-programming/)
2. [**LEGAL-BERT: The Muppets straight out of Law School**Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, et al.*2020 · 1,490 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2020-legal-bert-muppets-straight-out/)
3. [**RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,277 citations*](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/)
4. [**Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them**Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al.*2023 · 1,094 citations*](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/)
5. [**UNIFIEDQA: Crossing Format Boundaries with a Single QA System**Daniel Khashabi, Sewon Min, Tushar Khot, et al.*2020 · 843 citations*](https://md.research.elamur.ai/free/en/papers/khashabi-2020-unifiedqa-crossing-format-boundaries-single/)
6. [**Towards Reasoning in Large Language Models: A Survey**Jie Huang, Kevin Chen-Chuan Chang*2023 · 775 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/)
7. [**ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning**Ahmed Masry, Do Xuan Long, Jia Qing Tan, et al.*2022 · 744 citations*](https://md.research.elamur.ai/free/en/papers/masry-2022-chartqa-benchmark-question-answering-about/)
8. [**IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages**Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, et al.*2020 · 730 citations*](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/)
9. [**Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes**Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al.*2023 · 684 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/)
10. [**Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models**Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, et al.*2021 · 582 citations*](https://md.research.elamur.ai/free/en/papers/ni-2021-sentence-t5-scalable-sentence-encoders/)
11. [**PhoBERT: Pre-trained language models for Vietnamese**Dat Quoc Nguyen, Anh Tuan Nguyen*2020 · 571 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2020-phobert-pre-trained-language-models/)
12. [**AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models**Wanjun Zhong, Ruixiang Cui, Yiduo Guo, et al.*2024 · 563 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2024-agieval-human-centric-benchmark-evaluating/)
13. [**Document Ranking with a Pretrained Sequence-to-Sequence Model**Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, et al.*2020 · 538 citations*](https://md.research.elamur.ai/free/en/papers/nogueira-2020-document-ranking-pretrained-sequence-sequence/)
14. [**Evaluating Models' Local Decision Boundaries via Contrast Sets**Matt Gardner, Yoav Artzi, Victoria Basmov, et al.*2020 · 520 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/)
15. [**Sentiment Analysis in the Era of Large Language Models: A Reality Check**Wenxuan Zhang, Yue Deng, Bing Liu, et al.*2023 · 505 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-sentiment-analysis-era-large-language/)
16. [**Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis**Wenhao Zhu, Hongyi Liu, Qingxiu Dong, et al.*2024 · 503 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-multilingual-machine-translation-large-language/)
17. [**BBQ: A hand-built bias benchmark for question answering**Alicia Parrish, Angelica Chen, Nikita Nangia, et al.*2022 · 487 citations*](https://md.research.elamur.ai/free/en/papers/parrish-2022-bbq-hand-built-bias-benchmark/)
18. [**ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training**Weizhen Qi, Yu Yan, Yeyun Gong, et al.*2020 · 451 citations*](https://md.research.elamur.ai/free/en/papers/qi-2020-prophetnet-predicting-future-n-gram/)
19. [**GeDi: Generative Discriminator Guided Sequence Generation**Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, et al.*2021 · 444 citations*](https://md.research.elamur.ai/free/en/papers/krause-2021-gedi-generative-discriminator-guided-sequence/)
20. [**GPT-NER: Named Entity Recognition via Large Language Models**Shuhe Wang, Xiaofei Sun, Xiaoya Li, et al.*2025 · 436 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-gpt-ner-named-entity-recognition/)
21. [**Benchmarking Retrieval-Augmented Generation for Medicine**Guangzhi Xiong, Qiao Jin, Zhiyong Lu, et al.*2024 · 426 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2024-benchmarking-retrieval-augmented-generation-medicine/)
22. [**The Internal State of an LLM Knows When It's Lying**Amos Azaria, Tom Mitchell*2023 · 425 citations*](https://md.research.elamur.ai/free/en/papers/azaria-2023-internal-state-llm-knows-when/)
23. [**Template-Based Named Entity Recognition Using BART**Leyang Cui, Yu Wu, Jian Liu, et al.*2021 · 420 citations*](https://md.research.elamur.ai/free/en/papers/cui-2021-template-based-named-entity-recognition/)
24. [**ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning**Viet Dac Lai, Nghia Ngo, Amir Pouran Ben Veyseh, et al.*2023 · 418 citations*](https://md.research.elamur.ai/free/en/papers/lai-2023-chatgpt-beyond-english-comprehensive-evaluation/)
25. [**BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains**Yanis Labrak, Adrien Bazoge, Emmanuel Morin, et al.*2024 · 406 citations*](https://md.research.elamur.ai/free/en/papers/labrak-2024-biomistral-collection-open-source-pretrained/)
26. [**LongT5: Efficient Text-To-Text Transformer for Long Sequences**Mandy Guo, Joshua Ainslie, David Uthus, et al.*2022 · 398 citations*](https://md.research.elamur.ai/free/en/papers/guo-2022-longt5-efficient-text-text-transformer/)
27. [**Towards Mitigating LLM Hallucination via Self Reflection**Ziwei Ji, Tiezheng Yu, Yan Xu, et al.*2023 · 352 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-mitigating-llm-hallucination-self-reflection/)
28. [**SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities**Dong Zhang, Shimin Li, Xin Zhang, et al.*2023 · 346 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-speechgpt-empowering-large-language-models/)
29. [**Toxicity in chatgpt: Analyzing persona-assigned language models**Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, et al.*2023 · 344 citations*](https://md.research.elamur.ai/free/en/papers/deshpande-2023-toxicity-chatgpt-analyzing-persona-assigned/)
30. [**Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages**Wilhelmina Nekoto, Vukosi Marivate, Tshinondiwa Matsila, et al.*2020 · 333 citations*](https://md.research.elamur.ai/free/en/papers/nekoto-2020-participatory-research-low-resourced-machine/)
31. [**ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language**Oyvind Tafjord, Bhavana Dalvi, Peter Clark*2021 · 331 citations*](https://md.research.elamur.ai/free/en/papers/tafjord-2021-proofwriter-generating-implications-proofs-abductive/)
32. [**Large Language Models are Better Reasoners with Self-Verification**Yixuan Weng, Minjun Zhu, Fei Xia, et al.*2023 · 302 citations*](https://md.research.elamur.ai/free/en/papers/weng-2023-large-language-models-are-better/)
33. [**DialogSum: A Real-Life Scenario Dialogue Summarization Dataset**Yulong Chen, Yang Liu, Liang Chen, et al.*2021 · 301 citations*](https://md.research.elamur.ai/free/en/papers/chen-2021-dialogsum-real-life-scenario-dialogue/)
34. [**Multi-step Jailbreaking Privacy Attacks on ChatGPT**Haoran Li, Dadi Guo, Wei Fan, et al.*2023 · 298 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-multi-step-jailbreaking-privacy-attacks/)
35. [**Want To Reduce Labeling Cost? GPT-3 Can Help**Shuohang Wang, Yang Liu, Yichong Xu, et al.*2021 · 298 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-want-reduce-labeling-cost-gpt/)
36. [**Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting**Zhen Qin, Rolf Jagerman, Kai Hui, et al.*2024 · 296 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-large-language-models-are-effective/)
37. [**BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla**Abhik Bhattacharjee, Tahmid Hasan, Wasi Ahmad, et al.*2022 · 296 citations*](https://md.research.elamur.ai/free/en/papers/bhattacharjee-2022-banglabert-language-model-pretraining-benchmarks/)
38. [**Evaluating Verifiability in Generative Search Engines**Nelson Liu, Tianyi Zhang, Percy Liang*2023 · 294 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-verifiability-generative-search-engines/)
39. [**Text Classification via Large Language Models**Xiaofei Sun, Xiaoya Li, Jiwei Li, et al.*2023 · 292 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-text-classification-large-language-models/)
40. [**Can language models learn from explanations in context?**Andrew Lampinen, Ishita Dasgupta, Stephanie Chan, et al.*2022 · 288 citations*](https://md.research.elamur.ai/free/en/papers/lampinen-2022-can-language-models-learn-explanations/)
41. [**HuatuoGPT, Towards Taming Language Model to Be a Doctor**Hongbo Zhang, Junying Chen, Feng Jiang, et al.*2023 · 279 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-huatuogpt-taming-language-model-be/)
42. [**LLM-QAT: Data-Free Quantization Aware Training for Large Language Models**Zechun Liu, Barlas Oguz, Changsheng Zhao, et al.*2024 · 275 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-llm-qat-data-free-quantization/)
43. [**WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization**Faisal Ladhak, Esin Durmus, Claire Cardie, et al.*2020 · 272 citations*](https://md.research.elamur.ai/free/en/papers/ladhak-2020-wikilingua-new-benchmark-dataset-cross/)
44. [**GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation**Kang Min Yoo, Dongju Park, Jaewook Kang, et al.*2021 · 267 citations*](https://md.research.elamur.ai/free/en/papers/yoo-2021-gpt3mix-leveraging-large-scale-language/)
45. [**ConveRT: Efficient and Accurate Conversational Representations from Transformers**Matthew Henderson, Iñigo Casanueva, Nikola Mrkšić, et al.*2020 · 267 citations*](https://md.research.elamur.ai/free/en/papers/henderson-2020-convert-efficient-accurate-conversational-representations/)
46. [**Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing**Xi Victoria Lin, Richard Socher, Caiming Xiong*2020 · 261 citations*](https://md.research.elamur.ai/free/en/papers/lin-2020-bridging-textual-tabular-data-cross/)
47. [**TLDR: Extreme Summarization of Scientific Documents**Isabel Cachola, Kyle Lo, Arman Cohan, et al.*2020 · 260 citations*](https://md.research.elamur.ai/free/en/papers/cachola-2020-tldr-extreme-summarization-scientific-documents/)
48. [**Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions**Pouya Pezeshkpour, Estevam Hruschka*2023 · 256 citations*](https://md.research.elamur.ai/free/en/papers/pezeshkpour-2023-large-language-models-sensitivity-order/)
49. [**Few-shot Natural Language Generation for Task-Oriented Dialog**Baolin Peng, Chenguang Zhu, Chunyuan Li, et al.*2020 · 253 citations*](https://md.research.elamur.ai/free/en/papers/peng-2020-few-shot-natural-language-generation/)
50. [**Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers**Damai Dai, Yutao Sun, Li Dong, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-why-can-gpt-learn-context/)
51. [**Event Extraction as Multi-turn Question Answering**Fayuan Li, Weihua Peng, Yuguang Chen, et al.*2020 · 250 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-event-extraction-as-multi-turn/)
52. [**NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark**Oscar Sainz, Jon Campos, Iker García-Ferrero, et al.*2023 · 245 citations*](https://md.research.elamur.ai/free/en/papers/sainz-2023-nlp-evaluation-trouble-need-measure/)
53. [**MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning**Xiangru Tang, Anni Zou, Zhuosheng Zhang, et al.*2024 · 244 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-medagents-large-language-models-as/)
54. [**In-context Examples Selection for Machine Translation**Sweta Agrawal, Chunting Zhou, Mike Lewis, et al.*2023 · 241 citations*](https://md.research.elamur.ai/free/en/papers/agrawal-2023-context-examples-selection-machine-translation/)
55. [**On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey**Lin Long, Rui Wang, Ruixuan Xiao, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/long-2024-llms-driven-synthetic-data-generation/)
56. [**Reframing Instructional Prompts to GPTk's Language**Swaroop Mishra, Daniel Khashabi, Chitta Baral, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2022-reframing-instructional-prompts-gptk-s/)
57. [**Detecting Hallucinated Content in Conditional Neural Sequence Generation**Chunting Zhou, Graham Neubig, Jiatao Gu, et al.*2021 · 229 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2021-detecting-hallucinated-content-conditional-neural/)
58. [**HoVer: A Dataset for Many-Hop Fact Extraction And Claim Verification**Yichen Jiang, Shikha Bordia, Zheng Zhong, et al.*2020 · 221 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2020-hover-dataset-many-hop-fact/)
59. [**Multilingual Translation from Denoising Pre-Training**Yuqing Tang, Chau Tran, Xian Li, et al.*2021 · 219 citations*](https://md.research.elamur.ai/free/en/papers/tang-2021-multilingual-translation-denoising-pre-training/)
60. [**Distilling Reasoning Capabilities into Smaller Language Models**Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/)
61. [**Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models**Robert Logan IV, Ivana Balazevic, Eric Wallace, et al.*2022 · 215 citations*](https://md.research.elamur.ai/free/en/papers/iv-2022-cutting-down-prompts-parameters-simple/)
62. [**MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets**Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, et al.*2021 · 212 citations*](https://md.research.elamur.ai/free/en/papers/pramanick-2021-momenta-multimodal-framework-detecting-harmful/)
63. [**Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!**Yubo Ma, Yixin Cao, Yong Hong, et al.*2023 · 210 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-large-language-model-is-not/)
64. [**Sources of Hallucination by Large Language Models on Inference Tasks**Nick McKenna, Tianyi Li, Liang Cheng, et al.*2023 · 208 citations*](https://md.research.elamur.ai/free/en/papers/mckenna-2023-sources-hallucination-large-language-models/)
65. [**Challenges in Detoxifying Language Models**Johannes Welbl, Amelia Glaese, Jonathan Uesato, et al.*2021 · 206 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2021-challenges-detoxifying-language-models/)
66. [**Evaluating Factuality in Generation with Dependency-level Entailment**Tanya Goyal, Greg Durrett*2020 · 201 citations*](https://md.research.elamur.ai/free/en/papers/goyal-2020-evaluating-factuality-generation-dependency-level/)
67. [**A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/)
68. [**Generative Data Augmentation for Commonsense Reasoning**Yiben Yang, Chaitanya Malaviya, Jared Fernandez, et al.*2020 · 194 citations*](https://md.research.elamur.ai/free/en/papers/yang-2020-generative-data-augmentation-commonsense-reasoning/)
69. [**Towards Making the Most of ChatGPT for Machine Translation**Keqin Peng, Liang Ding, Qihuang Zhong, et al.*2023 · 190 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-making-most-chatgpt-machine-translation/)
70. [**A Survey on Stance Detection for Mis- and Disinformation Identification**Momchil Hardalov, Arnav Arora, Preslav Nakov, et al.*2022 · 187 citations*](https://md.research.elamur.ai/free/en/papers/hardalov-2022-survey-stance-detection-mis-disinformation/)
71. [**Large Language Models are few(1)-shot Table Reasoners**Wenhu Chen*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-large-language-models-are-few/)
72. [**Modeling Intra and Inter-modality Incongruity for Multi-Modal Sarcasm Detection**Hongliang Pan, Zheng Lin, Peng Fu, et al.*2020 · 181 citations*](https://md.research.elamur.ai/free/en/papers/pan-2020-modeling-intra-inter-modality-incongruity/)
73. [**Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization**Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao, et al.*2024 · 177 citations*](https://md.research.elamur.ai/free/en/papers/tseng-2024-two-tales-persona-llms-survey/)
74. [**On the Risk of Misinformation Pollution with Large Language Models**Yikang Pan, Liangming Pan, Wenhu Chen, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/pan-2023-risk-misinformation-pollution-large-language/)
75. [**ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts**Yuta Koreeda, Christopher Manning*2021 · 177 citations*](https://md.research.elamur.ai/free/en/papers/koreeda-2021-contractnli-dataset-document-level-natural/)
76. [**DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text**Jinyan Su, Terry Zhuo, Di Wang, et al.*2023 · 172 citations*](https://md.research.elamur.ai/free/en/papers/su-2023-detectllm-leveraging-log-rank-information/)
77. [**P-Stance: A Large Dataset for Stance Detection in Political Domain**Yingjie Li, Tiberiu Sosea, Aditya Sawant, et al.*2021 · 169 citations*](https://md.research.elamur.ai/free/en/papers/li-2021-p-stance-large-dataset-stance/)
78. [**Towards Controllable Biases in Language Generation**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2020 · 166 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2020-controllable-biases-language-generation/)
79. [**Sustainable Modular Debiasing of Language Models**Anne Lauscher, Tobias Lueken, Goran Glavaš*2021 · 165 citations*](https://md.research.elamur.ai/free/en/papers/lauscher-2021-sustainable-modular-debiasing-language-models/)
80. [**Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/)
81. [**LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression**Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/pan-2024-llmlingua-2-data-distillation-efficient/)
82. [**The Lessons of Developing Process Reward Models in Mathematical Reasoning**Zhenru Zhang, Chujie Zheng, Yangzhen Wu, et al.*2025 · 155 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-lessons-developing-process-reward-models/)
83. [**Large Language Models with Controllable Working Memory**Daliang Li, Ankit Singh Rawat, Manzil Zaheer, et al.*2023 · 154 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-large-language-models-controllable-working/)
84. [**Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding**Heming Xia, Zhe Yang, Qingxiu Dong, et al.*2024 · 145 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-unlocking-efficiency-large-language-model/)
85. [**Context-faithful Prompting for Large Language Models**Wenxuan Zhou, Sheng Zhang, Hoifung Poon, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-context-faithful-prompting-large-language/)
86. [**In-Context Learning for Few-Shot Dialogue State Tracking**Yushi Hu, Chia-Hsuan Lee, Tianbao Xie, et al.*2022 · 144 citations*](https://md.research.elamur.ai/free/en/papers/hu-2022-context-learning-few-shot-dialogue/)
87. [**UNQOVERing Stereotyping Biases via Underspecified Questions**Tao Li, Daniel Khashabi, Tushar Khot, et al.*2020 · 144 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-unqovering-stereotyping-biases-underspecified-questions/)
88. [**Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts**Haoxiang Wang, Wei Xiong, Tengyang Xie, et al.*2024 · 142 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-interpretable-preferences-multi-objective-reward/)
89. [**In-Context Learning Creates Task Vectors**Roee Hendel, Mor Geva, Amir Globerson*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/hendel-2023-context-learning-creates-task-vectors/)
90. [**Byte Pair Encoding is Suboptimal for Language Model Pretraining**Kaj Bostrom, Greg Durrett*2020 · 136 citations*](https://md.research.elamur.ai/free/en/papers/bostrom-2020-byte-pair-encoding-is-suboptimal/)
91. [**Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models**Qingyu Lu, Baopu Qiu, Liang Ding, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-error-analysis-prompting-enables-human/)
92. [**Few-Shot Self-Rationalization with Natural Language Prompts**Ana Marasovic, Iz Beltagy, Doug Downey, et al.*2022 · 134 citations*](https://md.research.elamur.ai/free/en/papers/marasovic-2022-few-shot-self-rationalization-natural/)
93. [**Enhancing Zero-shot and Few-shot Stance Detection with Commonsense Knowledge Graph**Rui Liu, Zheng Lin, Yutong Tan, et al.*2021 · 134 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-enhancing-zero-shot-few-shot/)
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