---
title: "Who cites Varsha Kishore — page 2 — 3,883 citing papers"
description: "3,883 papers of the Elamur Research library cite the works of Varsha Kishore, found in their bibliographies · page 2 of 7 · 101–200; the most…"
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---
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# Who cites Varsha Kishore

3,892 citations of their works from 3,883 papers of our library (of the 149,439 whose bibliographies we read) · 625 with a page here, the most cited first · page 2 of 7 · 101–200 · [← Varsha Kishore](https://md.research.elamur.ai/free/en/authors/varsha-kishore/)

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101. **[Fill in the BLANC: Human-free quality estimation of document summaries](https://md.research.elamur.ai/free/en/papers/vasilyev-2020-fill-blanc-human-free-quality/) [Workshop on Evaluation and Comparison of NLP Systems](https://md.research.elamur.ai/free/en/venues/eval4nlp/)**Oleg Vasilyev, Vedant Dharnidharka, John Bohannon*2020 · 155 citations*
102. **[Scientific Large Language Models: A Survey on Biological & Chemical Domains](https://md.research.elamur.ai/free/en/papers/zhang-2024-scientific-large-language-models/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Qiang Zhang, Keyang Ding, Tianwen Lyv, et al.*2024 · 154 citations*
103. **[The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-dawn-after-dark-empirical-study/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyi Li, Jie Chen, Ruiyang Ren, et al.*2024 · 153 citations*
104. [**Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigation arXiv:2305.15852**Niels Mundler, Jingxuan He, Slobodan Jenko, et al.*2023 · 152 citations*](https://md.research.elamur.ai/free/en/papers/mundler-2023-self-contradictory-hallucinations-large-language/)
105. **[Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text](https://md.research.elamur.ai/free/en/papers/dou-2022-is-gpt-3-text-indistinguishable/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, et al.*2022 · 152 citations*
106. **[Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary](https://md.research.elamur.ai/free/en/papers/deutsch-2021-question-answering-as-automatic-metric/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Daniel Deutsch, Tania Bedrax-Weiss, Dan Roth*2021 · 152 citations*
107. **[Investigating Pretrained Language Models for Graph-to-Text Generation](https://md.research.elamur.ai/free/en/papers/ribeiro-2021-investigating-pretrained-language-models-graph/) [Workshop on Natural Language Processing for Conversational AI](https://md.research.elamur.ai/free/en/venues/nlp4convai/)**Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, et al.*2021 · 151 citations*
108. **[Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering](https://md.research.elamur.ai/free/en/papers/adlakha-2024-evaluating-correctness-faithfulness-instruction-following/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, et al.*2024 · 149 citations*
109. **[Looking for a Needle in a Haystack: A Comprehensive Study of Hallucinations in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/guerreiro-2023-looking-needle-haystack-comprehensive-study/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Nuno M. Guerreiro, Elena Voita, André Martins*2023 · 149 citations*
110. **[Instruction Induction: From Few Examples to Natural Language Task Descriptions](https://md.research.elamur.ai/free/en/papers/honovich-2022-instruction-induction-few-examples-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Or Honovich, Uri Shaham, Samuel R. Bowman, et al.*2022 · 148 citations*
111. **[Measure and Improve Robustness in NLP Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2022-measure-improve-robustness-nlp-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xuezhi Wang, Haohan Wang, Diyi Yang*2022 · 148 citations*
112. **[Quality-Aware Decoding for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/fernandes-2022-quality-aware-decoding-neural-machine/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Patrick Fernandes, António Farinhas, Ricardo Rei, et al.*2022 · 146 citations*
113. **[Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks](https://md.research.elamur.ai/free/en/papers/mireshghallah-2022-quantifying-privacy-risks-masked-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, et al.*2022 · 146 citations*
114. [**JudgeLM: Fine-tuned Large Language Models are Scalable Judges arXiv:2310.17631**Lianghui Zhu, Xinggang Wang, Xinlong Wang*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-judgelm-fine-tuned-large-language/)
115. **[GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems](https://md.research.elamur.ai/free/en/papers/huang-2020-grade-automatic-graph-enhanced-coherence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lishan Huang, Zheng Ye, Jinghui Qin, et al.*2020 · 144 citations*
116. **[What Have We Achieved on Text Summarization?](https://md.research.elamur.ai/free/en/papers/huang-2020-what-have-we-achieved-text/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Dandan Huang, Leyang Cui, Sen Yang, et al.*2020 · 143 citations*
117. **[Dense X Retrieval: What Retrieval Granularity Should We Use?](https://md.research.elamur.ai/free/en/papers/chen-2024-dense-x-retrieval-what-retrieval/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tong Chen, Hongwei Wang, Sihao Chen, et al.*2024 · 142 citations*
118. **[On The Evaluation of Machine Translation Systems Trained With Back-Translation](https://md.research.elamur.ai/free/en/papers/edunov-2020-evaluation-machine-translation-systems-trained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sergey Edunov, Myle Ott, Marc'Aurelio Ranzato, et al.*2020 · 140 citations*
119. **[All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality](https://md.research.elamur.ai/free/en/papers/timkey-2021-all-bark-no-bite-rogue/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**William Timkey, Marten van Schijndel*2021 · 139 citations*
120. **[FaithDial: A Faithful Benchmark for Information-Seeking Dialogue](https://md.research.elamur.ai/free/en/papers/dziri-2022-faithdial-faithful-benchmark-information-seeking/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Nouha Dziri, Ehsan Kamalloo, Sivan Milton, et al.*2022 · 138 citations*
121. **[Affective and Contextual Embedding for Sarcasm Detection](https://md.research.elamur.ai/free/en/papers/babanejad-2020-affective-contextual-embedding-sarcasm-detection/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Nastaran Babanejad, Heidar Davoudi, Aijun An, et al.*2020 · 135 citations*
122. [**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/)
123. **[Few-Shot Self-Rationalization with Natural Language Prompts](https://md.research.elamur.ai/free/en/papers/marasovic-2022-few-shot-self-rationalization-natural/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ana Marasovic, Iz Beltagy, Doug Downey, et al.*2022 · 134 citations*
124. **[Humans or LLMs as the Judge? A Study on Judgement Bias](https://md.research.elamur.ai/free/en/papers/chen-2024-humans-or-llms-as-judge/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Guiming Hardy Chen, Shunian Chen, Ziche Liu, et al.*2024 · 133 citations*
125. **[FLUTE: Figurative Language Understanding through Textual Explanations](https://md.research.elamur.ai/free/en/papers/chakrabarty-2022-flute-figurative-language-understanding-through/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, et al.*2022 · 133 citations*
126. **[MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems](https://md.research.elamur.ai/free/en/papers/macina-2023-mathdial-dialogue-tutoring-dataset-rich/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jakub Macina, Nico Daheim, Sankalan Chowdhury, et al.*2023 · 131 citations*
127. **[SciMON: Scientific Inspiration Machines Optimized for Novelty](https://md.research.elamur.ai/free/en/papers/wang-2023-scimon-scientific-inspiration-machines/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingyun Wang, Doug Downey, Heng Ji, et al.*2023 · 131 citations*
128. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
129. **[GO FIGURE: A Meta Evaluation of Factuality in Summarization](https://md.research.elamur.ai/free/en/papers/gabriel-2021-go-figure-meta-evaluation-factuality/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Saadia Gabriel, Asli Celikyilmaz, Rahul Jha, et al.*2021 · 130 citations*
130. **[Learning Compact Metrics for MT](https://md.research.elamur.ai/free/en/papers/pu-2021-learning-compact-metrics-mt/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Amy Pu, Hyung Won Chung, Ankur Parikh, et al.*2021 · 130 citations*
131. **[Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration](https://md.research.elamur.ai/free/en/papers/deng-2023-prompting-evaluating-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yang Deng, Lizi Liao, Liang Chen, et al.*2023 · 129 citations*
132. [**Human-like Summarization Evaluation with ChatGPT arXiv:2304.02554**Mingqi Gao, Jie Ruan, Renliang Sun, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-human-like-summarization-evaluation-chatgpt/)
133. [**A Comprehensive Assessment of Dialog Evaluation Metrics The First Workshop on Evaluations and Assessments of Neural Conversation Systems**Yi-Ting Yeh, Maxine Eskenazi, Shikib Mehri*2021 · 128 citations*](https://md.research.elamur.ai/free/en/papers/yeh-2021-comprehensive-assessment-dialog-evaluation-metrics/)
134. **[Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors](https://md.research.elamur.ai/free/en/papers/tang-2023-understanding-factual-errors-summarization-errors/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Liyan Tang, Tanya Goyal, Alex Fabbri, et al.*2023 · 126 citations*
135. [**Generative Judge for Evaluating Alignment arXiv:2310.05470**Junlong Li, Shichao Sun, Weizhe Yuan, et al.*2023 · 125 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-generative-judge-evaluating-alignment/)
136. **[The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems](https://md.research.elamur.ai/free/en/papers/ziems-2022-moral-integrity-corpus-benchmark-ethical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Caleb Ziems, Jane A. Yu, Yi-Chia Wang, et al.*2022 · 122 citations*
137. [**Hallucination of Multimodal Large Language Models: A Survey arXiv:2404.18930**Zechen Bai, Pichao Wang, Tianjun Xiao, et al.*2024 · 120 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-hallucination-multimodal-large-language-models/)
138. **[Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-models-are-not/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, et al.*2023 · 119 citations*
139. **[A Critical Evaluation of Evaluations for Long-form Question Answering](https://md.research.elamur.ai/free/en/papers/xu-2023-critical-evaluation-evaluations-long-form/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fangyuan Xu, Yixiao Song, Mohit Iyyer, et al.*2023 · 117 citations*
140. **[How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN](https://md.research.elamur.ai/free/en/papers/mccoy-2023-how-much-do-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**R. Thomas McCoy, Paul Smolensky, Tal Linzen, et al.*2023 · 117 citations*
141. [**Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation arXiv:2212.07981**Yixin Liu, Alexander R. Fabbri, Pengfei Liu, et al.*2022 · 117 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-revisiting-gold-standard-grounding-summarization/)
142. **[A Statistical Analysis of Summarization Evaluation Metrics Using Resampling Methods](https://md.research.elamur.ai/free/en/papers/deutsch-2021-statistical-analysis-summarization-evaluation-metrics/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Daniel Deutsch, Rotem Dror, Dan Roth*2021 · 117 citations*
143. [**Med-Flamingo: a Multimodal Medical Few-shot Learner arXiv:2307.15189**Michael Moor, Qian Huang, Shirley Wu, et al.*2023 · 116 citations*](https://md.research.elamur.ai/free/en/papers/moor-2023-med-flamingo-multimodal-medical-few/)
144. **[LongEval: Guidelines for Human Evaluation of Faithfulness in Long-form Summarization](https://md.research.elamur.ai/free/en/papers/krishna-2023-longeval-guidelines-human-evaluation-faithfulness/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Kalpesh Krishna, Erin Bransom, Bailey Kuehl, et al.*2023 · 114 citations*
145. **[Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study](https://md.research.elamur.ai/free/en/papers/chen-2023-exploring-use-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yi Chen, Rui Wang, Haiyun Jiang, et al.*2023 · 113 citations*
146. **[Unlimiformer: Long-Range Transformers with Unlimited Length Input](https://md.research.elamur.ai/free/en/papers/bertsch-2023-unlimiformer-long-range-transformers-unlimited/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Amanda Bertsch, Uri Alon, Graham Neubig, et al.*2023 · 111 citations*
147. **[Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/lin-2021-common-sense-beyond-english-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bill Yuchen Lin, Seyeon Lee, Xiaoyang Qiao, et al.*2021 · 107 citations*
148. **[Benchmarking Foundation Models with Language-Model-as-an-Examiner](https://md.research.elamur.ai/free/en/papers/bai-2023-benchmarking-foundation-models-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yushi Bai, Jiahao Ying, Yixin Cao, et al.*2023 · 106 citations*
149. **[Complementary Explanations for Effective In-Context Learning](https://md.research.elamur.ai/free/en/papers/ye-2023-complementary-explanations-effective-context-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, et al.*2023 · 106 citations*
150. **[Evaluating the Evaluation of Diversity in Natural Language Generation](https://md.research.elamur.ai/free/en/papers/tevet-2021-evaluating-evaluation-diversity-natural-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Guy Tevet, Jonathan Berant*2021 · 106 citations*
151. **[A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations](https://md.research.elamur.ai/free/en/papers/laskar-2024-systematic-survey-critical-review-evaluating/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, et al.*2024 · 105 citations*
152. **[LENS: A Learnable Evaluation Metric for Text Simplification](https://md.research.elamur.ai/free/en/papers/maddela-2023-lens-learnable-evaluation-metric-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mounica Maddela, Yao Dou, David Heineman, et al.*2023 · 104 citations*
153. **[Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation](https://md.research.elamur.ai/free/en/papers/deng-2021-compression-transduction-creation-unified-framework/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Mingkai Deng, Bowen Tan, Zhengzhong Liu, et al.*2021 · 102 citations*
154. **[Evaluating Attribution in Dialogue Systems: The BEGIN Benchmark](https://md.research.elamur.ai/free/en/papers/dziri-2022-evaluating-attribution-dialogue-systems-begin/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Nouha Dziri, Hannah Rashkin, Tal Linzen, et al.*2022 · 100 citations*
155. **[Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation](https://md.research.elamur.ai/free/en/papers/bulian-2022-tomayto-tomahto-beyond-token-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jannis Bulian, Christian Buck, Wojciech Gajewski, et al.*2022 · 100 citations*
156. **[UniTE: Unified Translation Evaluation](https://md.research.elamur.ai/free/en/papers/wan-2022-unite-unified-translation-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yu Wan, Dayiheng Liu, Baosong Yang, et al.*2022 · 100 citations*
157. **[Unbabel's Participation in the WMT20 Metrics Shared Task](https://md.research.elamur.ai/free/en/papers/rei-2020-unbabel-s-participation-wmt20-metrics/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Ricardo Rei, Craig Stewart, Ana C Farinha, et al.*2020 · 98 citations*
158. [**A Survey of Confidence Estimation and Calibration in Large Language Models arXiv:2311.08298**Jiahui Geng, Fengyu Cai, Yuxia Wang, et al.*2023 · 97 citations*](https://md.research.elamur.ai/free/en/papers/geng-2023-survey-confidence-estimation-calibration-large/)
159. **[SMILE: Single-turn to Multi-turn Inclusive Language Expansion via ChatGPT for Mental Health Support](https://md.research.elamur.ai/free/en/papers/qiu-2024-smile-single-turn-multi-turn/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Huachuan Qiu, Hongliang He, Shuai Zhang, et al.*2024 · 95 citations*
160. **[WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences](https://md.research.elamur.ai/free/en/papers/liu-2023-webglm-efficient-web-enhanced/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Xiao Liu, Hanyu Lai, Hao Yu, et al.*2023 · 95 citations*
161. **[On Learning to Summarize with Large Language Models as References](https://md.research.elamur.ai/free/en/papers/liu-2024-learning-summarize-large-language-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Yixin Liu, Kejian Shi, Katherine He, et al.*2024 · 94 citations*
162. **[FineSurE: Fine-grained Summarization Evaluation using LLMs](https://md.research.elamur.ai/free/en/papers/song-2024-finesure-fine-grained-summarization-evaluation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hwanjun Song, Hang Su, Igor Shalyminov, et al.*2024 · 92 citations*
163. **[f-Divergence Minimization for Sequence-Level Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/wen-2023-f-divergence-minimization-sequence-level/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuqiao Wen, Zichao Li, Wenyu Du, et al.*2023 · 92 citations*
164. [**Universal Self-Consistency for Large Language Model Generation arXiv:2311.17311**Xinyun Chen, Renat Aksitov, Uri Alon, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-universal-self-consistency-large-language/)
165. [**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/)
166. [**Generationary or «How We Went beyond Word Sense Inventories and Learned to Gloss»**Michele Bevilacqua, Marco Maru, Roberto Navigli*2020 · 90 citations*](https://md.research.elamur.ai/free/en/papers/bevilacqua-2020-generationary/)
167. [**In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering arXiv:2311.06668**Sheng Liu, Haotian Ye, Lei Xing, et al.*2023 · 89 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-context-vectors-making-context-learning/)
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169. **[LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models](https://md.research.elamur.ai/free/en/papers/liusie-2024-llm-comparative-assessment-zero-shot/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Adian Liusie, Potsawee Manakul, Mark Gales*2024 · 87 citations*
170. [**A Survey of Large Language Models in Medicine: Progress, Application, and Challenge arXiv:2311.05112**Hongjian Zhou, Fenglin Liu, Boyang Gu, et al.*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-survey-large-language-models-medicine/)
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172. **[Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment](https://md.research.elamur.ai/free/en/papers/raina-2024-is-llm-as-judge-robust/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vyas Raina, Adian Liusie, Mark Gales*2024 · 86 citations*
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174. **[Are References Really Needed? Unbabel-IST 2021 Submission for the Metrics Shared Task](https://md.research.elamur.ai/free/en/papers/rei-2021-are-references-really-needed-unbabel/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Ricardo Rei, Ana C Farinha, Chrysoula Zerva, et al.*2021 · 84 citations*
175. [**PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations arXiv:2307.02762**Ruosen Li, Teerth Patel, Xinya Du*2023 · 83 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-prd-peer-rank-discussion-improve/)
176. [**Does Writing with Language Models Reduce Content Diversity? arXiv:2309.05196**Vishakh Padmakumar, He He*2023 · 82 citations*](https://md.research.elamur.ai/free/en/papers/padmakumar-2023-writing-language-models-reduce-content/)
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178. **[Chain-of-Dictionary Prompting Elicits Translation in Large Language Models](https://md.research.elamur.ai/free/en/papers/lu-2024-chain-dictionary-prompting-elicits-translation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hongyuan Lu, Haoran Yang, Haoyang Huang, et al.*2024 · 81 citations*
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180. **[Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps](https://md.research.elamur.ai/free/en/papers/chuang-2024-lookback-lens-detecting-mitigating-contextual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh, et al.*2024 · 78 citations*
181. [**Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators arXiv:2403.16950**Yinhong Liu, Han Zhou, Zhijiang Guo, et al.*2024 · 76 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-aligning-human-judgement-role-pairwise/)
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185. [**Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers arXiv:2404.04925**Libo Qin, Qiguang Chen, Yuhang Zhou, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-multilingual-large-language-model-survey/)
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187. [**Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization arXiv:2305.13091 · Findings of EMNLP 2023**Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-models-yet-human/)
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190. [**AlignBench: Benchmarking Chinese Alignment of Large Language Models arXiv:2311.18743**Xiao Liu, Xuanyu Lei, Shengyuan Wang, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-alignbench-benchmarking-chinese-alignment/)
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192. **[LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing](https://md.research.elamur.ai/free/en/papers/du-2024-llms-assist-nlp-researchers-critique/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiangshu Du, Yibo Wang, Wenting Zhao, et al.*2024 · 71 citations*
193. **[Automatic Generation of Socratic Subquestions for Teaching Math Word Problems](https://md.research.elamur.ai/free/en/papers/shridhar-2022-automatic-generation-socratic-subquestions-teaching/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kumar Shridhar, Jakub Macina, Mennatallah El-Assady, et al.*2022 · 70 citations*
194. **[Embarrassingly Easy Document-Level MT Metrics: How to Convert Any Pretrained Metric into a Document-Level Metric](https://md.research.elamur.ai/free/en/papers/vernikos-2022-embarrassingly-easy-document-level-mt/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Giorgos Vernikos, Brian Thompson, Prashant Mathur, et al.*2022 · 68 citations*
195. **[On Context Utilization in Summarization with Large Language Models](https://md.research.elamur.ai/free/en/papers/ravaut-2024-context-utilization-summarization-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mathieu Ravaut, Aixin Sun, Nancy Chen, et al.*2024 · 67 citations*
196. **[AutoTutor meets Large Language Models: A Language Model Tutor with Rich Pedagogy and Guardrails](https://md.research.elamur.ai/free/en/papers/chowdhury-2024-autotutor-meets-large-language-models/) [ACM Conference on Learning @ Scale (L@S)](https://md.research.elamur.ai/free/en/venues/acm-conference-on-learning-scale-l-s/)**Sankalan Pal Chowdhury, Vilém Zouhar, Mrinmaya Sachan*2024 · 66 citations*
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198. [**Large Language Model Routing with Benchmark Datasets arXiv:2309.15789**Tal Shnitzer, Anthony Ou, Mirian Silva, et al.*2023 · 63 citations*](https://md.research.elamur.ai/free/en/papers/shnitzer-2023-large-language-model-routing-benchmark/)
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200. **[LegalAgentBench: Evaluating LLM Agents in Legal Domain](https://md.research.elamur.ai/free/en/papers/li-2025-legalagentbench-evaluating-llm-agents-legal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haitao Li, Junjie Chen, Jingli Yang, et al.*2025 · 62 citations*

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