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title: "Who cites ROUGE: A Package for Automatic Evaluation of…"
description: "5,641 papers of the Elamur Research library cite «ROUGE: A Package for Automatic Evaluation of Summaries», found in their bibliographies · page 2 of 3 ·…"
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# Who cites «ROUGE: A Package for Automatic Evaluation of Summaries»

5,641 papers of our library cite it (of the 140,884 whose bibliographies we read) · 300 with a page here, the most cited first · page 2 of 3 · 101–200 · [← the paper](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/)

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101. [**Learning Neural Templates for Text Generation**Sam Wiseman, Stuart Shieber, Alexander Rush*2018 · 279 citations*](https://md.research.elamur.ai/free/en/papers/wiseman-2018-learning-neural-templates-text-generation/)
102. [**TRUE: Re-evaluating Factual Consistency Evaluation**Or Honovich, Roee Aharoni, Jonathan Herzig, et al.*2022 · 276 citations*](https://md.research.elamur.ai/free/en/papers/honovich-2022-true-re-evaluating-factual-consistency/)
103. [**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/)
104. [**Prometheus: Inducing Fine-grained Evaluation Capability in Language Models**Seungone Kim, Jamin Shin, Yejin Cho, et al.*2023 · 267 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-prometheus-inducing-fine-grained-evaluation/)
105. [**Handling Divergent Reference Texts when Evaluating Table-to-Text Generation**Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, et al.*2019 · 266 citations*](https://md.research.elamur.ai/free/en/papers/dhingra-2019-handling-divergent-reference-texts-when/)
106. [**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/)
107. [**CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization**Shuyang Cao, Lu Wang*2021 · 257 citations*](https://md.research.elamur.ai/free/en/papers/cao-2021-cliff-contrastive-learning-improving-faithfulness/)
108. [**Re-evaluating Evaluation in Text Summarization**Manik Bhandari, Pranav Narayan Gour, Atabak Ashfaq, et al.*2020 · 256 citations*](https://md.research.elamur.ai/free/en/papers/bhandari-2020-re-evaluating-evaluation-text-summarization/)
109. [**Automatically Assessing Machine Summary Content Without a Gold Standard**Annie Louis, Ani Nenkova*2013 · 254 citations*](https://md.research.elamur.ai/free/en/papers/louis-2013-automatically-assessing-machine-summary-content/)
110. [**Multi-Reward Reinforced Summarization with Saliency and Entailment**Ramakanth Pasunuru, Mohit Bansal*2018 · 252 citations*](https://md.research.elamur.ai/free/en/papers/pasunuru-2018-multi-reward-reinforced-summarization-saliency/)
111. [**ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation**Vijit Malik, Rishabh Sanjay, Shubham Kumar Nigam, et al.*2021 · 251 citations*](https://md.research.elamur.ai/free/en/papers/malik-2021-ildc-cjpe-indian-legal-documents/)
112. [**SPICE: Semantic Propositional Image Caption Evaluation**Peter Anderson, Basura Fernando, Mark Johnson, et al.*2016 · 244 citations*](https://md.research.elamur.ai/free/en/papers/anderson-2016-spice-semantic-propositional-image-caption/)
113. [**MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers**Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al.*2021 · 243 citations*](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/)
114. [**Trusting Your Evidence: Hallucinate Less with Context-aware Decoding**Weijia Shi, Xiaochuang Han, Mike Lewis, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/)
115. [**Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports**Yuhao Zhang, Derek Merck, Emily Tsai, et al.*2020 · 240 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-optimizing-factual-correctness-summary-study/)
116. [**LaMP: When Large Language Models Meet Personalization**Alireza Salemi, Sheshera Mysore, Michael Bendersky, et al.*2024 · 239 citations*](https://md.research.elamur.ai/free/en/papers/salemi-2024-lamp-when-large-language-models/)
117. [**ASQA: Factoid Questions Meet Long-Form Answers**Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, et al.*2022 · 239 citations*](https://md.research.elamur.ai/free/en/papers/stelmakh-2022-asqa-factoid-questions-meet-long/)
118. [**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/)
119. [**COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs**Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, et al.*2020 · 235 citations*](https://md.research.elamur.ai/free/en/papers/hwang-2020-comet-atomic-2020-symbolic-neural/)
120. [**Politeness Transfer: A Tag and Generate Approach**Aman Madaan, Amrith Setlur, Tanmay Parekh, et al.*2020 · 234 citations*](https://md.research.elamur.ai/free/en/papers/madaan-2020-politeness-transfer-tag-generate-approach/)
121. [**A Discrete Hard EM Approach for Weakly Supervised Question Answering**Sewon Min, Danqi Chen, Hannaneh Hajishirzi, et al.*2019 · 233 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-discrete-hard-em-approach-weakly/)
122. [**Classical Structured Prediction Losses for Sequence to Sequence Learning**Sergey Edunov, Myle Ott, Michael Auli, et al.*2018 · 232 citations*](https://md.research.elamur.ai/free/en/papers/edunov-2018-classical-structured-prediction-losses-sequence/)
123. [**Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation**Amit Moryossef, Yoav Goldberg, Ido Dagan*2019 · 230 citations*](https://md.research.elamur.ai/free/en/papers/moryossef-2019-step-step-separating-planning-realization/)
124. [**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/)
125. [**Hurdles to Progress in Long-form Question Answering**Kalpesh Krishna, Aurko Roy, Mohit Iyyer*2021 · 227 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2021-hurdles-progress-long-form-question/)
126. [**Open-Domain Question Answering Goes Conversational via Question Rewriting**Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, et al.*2021 · 227 citations*](https://md.research.elamur.ai/free/en/papers/anantha-2021-open-domain-question-answering-goes/)
127. [**TrustLLM: Trustworthiness in Large Language Models**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
128. [**Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting**Haoyang Huang, Tianyi Tang, Dongdong Zhang, et al.*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-all-languages-created-equal-llms/)
129. [**Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models**Seungone Kim, Juyoung Suk, Shayne Longpre, et al.*2024 · 220 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-prometheus-2-open-source-language/)
130. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 219 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
131. [**Factual Error Correction for Abstractive Summarization Models**Meng Cao, Yue Dong, Jiapeng Wu, et al.*2020 · 217 citations*](https://md.research.elamur.ai/free/en/papers/cao-2020-factual-error-correction-abstractive-summarization/)
132. [**Data-Driven Sentence Simplification: Survey and Benchmark**Fernando Alva-Manchego, Carolina Scarton, Lucia Specia*2020 · 213 citations*](https://md.research.elamur.ai/free/en/papers/manchego-2020-data-driven-sentence-simplification-survey/)
133. [**Fine-Grained Human Feedback Gives Better Rewards for Language Model Training**Zeqiu Wu, Yushi Hu, Weijia Shi, et al.*2023 · 212 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-fine-grained-human-feedback-gives/)
134. [**Answers Unite! Unsupervised Metrics for Reinforced Summarization Models**Thomas Scialom, Sylvain Lamprier, Benjamin Piwowarski, et al.*2019 · 210 citations*](https://md.research.elamur.ai/free/en/papers/scialom-2019-answers-unite-unsupervised-metrics-reinforced/)
135. [**Compressing Context to Enhance Inference Efficiency of Large Language Models**Yucheng Li, Bo Dong, Frank Guerin, et al.*2023 · 208 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-compressing-context-enhance-inference-efficiency/)
136. [**LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models**Yen-Ting Lin, Yun-Nung Chen*2023 · 206 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-llm-eval-unified-multi-dimensional/)
137. [**PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization**Wen Xiao, Iz Beltagy, Giuseppe Carenini, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2022-primera-pyramid-based-masked-sentence/)
138. [**Towards a Unified Multi-Dimensional Evaluator for Text Generation**Ming Zhong, Yang Liu, Da Yin, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2022-unified-multi-dimensional-evaluator-text/)
139. [**AraT5: Text-to-Text Transformers for Arabic Language Generation**El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed*2022 · 201 citations*](https://md.research.elamur.ai/free/en/papers/nagoudi-2022-arat5-text-text-transformers-arabic/)
140. [**Aligning Large Language Models with Human: A Survey**Yufei Wang, Wanjun Zhong, Liangyou Li, et al.*2023 · 199 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/)
141. [**CTRLsum: Towards Generic Controllable Text Summarization**Junxian He, Wojciech Kryscinski, Bryan McCann, et al.*2022 · 199 citations*](https://md.research.elamur.ai/free/en/papers/he-2022-ctrlsum-generic-controllable-text-summarization/)
142. [**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/)
143. [**Q²: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering**Or Honovich, Leshem Choshen, Roee Aharoni, et al.*2021 · 195 citations*](https://md.research.elamur.ai/free/en/papers/honovich-2021-q-2-evaluating-factual-consistency/)
144. [**LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods**Haitao Li, Qian Dong, Junjie Chen, et al.*2024 · 188 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llms-judges-comprehensive-survey-llm/)
145. [**RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models**Zekun Moore Wang, Zhongyuan Peng, Haoran Que, et al.*2023 · 186 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-rolellm-benchmarking-eliciting-enhancing-role/)
146. [**We Need to Consider Disagreement in Evaluation**Valerio Basile, Michael Fell, Tommaso Fornaciari, et al.*2021 · 185 citations*](https://md.research.elamur.ai/free/en/papers/basile-2021-we-need-consider-disagreement-evaluation/)
147. [**compare-mt: A Tool for Holistic Comparison of Language Generation Systems**Graham Neubig, Zi-Yi Dou, Junjie Hu, et al.*2019 · 185 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2019-compare-mt-tool-holistic-comparison/)
148. [**LawBench: Benchmarking Legal Knowledge of Large Language Models**Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.*2023 · 184 citations*](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/)
149. [**Re2G: Retrieve, Rerank, Generate**Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, et al.*2022 · 183 citations*](https://md.research.elamur.ai/free/en/papers/glass-2022-re2g-retrieve-rerank-generate/)
150. [**Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs**Houyu Zhang, Zhenghao Liu, Chenyan Xiong, et al.*2020 · 181 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-grounded-conversation-generation-as-guided/)
151. [**MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization**Chenguang Zhu, Yang Liu, Jie Mei, et al.*2021 · 176 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2021-mediasum-large-scale-media-interview/)
152. [**AudioCaps: Generating Captions for Audios in The Wild**Chris Dongjoo Kim, Byeongchang Kim, Hyunmin Lee, et al.*2019 · 176 citations*](https://md.research.elamur.ai/free/en/papers/kim-2019-audiocaps-generating-captions-audios-wild/)
153. [**BillSum: A Corpus for Automatic Summarization of US Legislation**Anastassia Kornilova, Vladimir Eidelman*2019 · 173 citations*](https://md.research.elamur.ai/free/en/papers/kornilova-2019-billsum-corpus-automatic-summarization-us/)
154. [**Controllable Paraphrase Generation with a Syntactic Exemplar**Mingda Chen, Qingming Tang, Sam Wiseman, et al.*2019 · 173 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-controllable-paraphrase-generation-syntactic-exemplar/)
155. [**A-MEM: Agentic Memory for LLM Agents**Wujiang Xu, Zujie Liang, Kai Mei, et al.*2025 · 171 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-mem-agentic-memory-llm-agents/)
156. [**AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning**Qingru Zhang, Minshuo Chen, Alexander Bukharin, et al.*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-adalora-adaptive-budget-allocation-parameter/)
157. [**Learning to Compress Prompts with Gist Tokens**Jesse Mu, Xiang Lisa Li, Noah Goodman*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/mu-2023-learning-compress-prompts-gist-tokens/)
158. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
159. [**Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization**Meng Cao, Yue Dong, Jackie Cheung*2022 · 167 citations*](https://md.research.elamur.ai/free/en/papers/cao-2022-hallucinated-but-factual-inspecting-factuality/)
160. [**Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization**Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kiante Brantley, et al.*2022 · 166 citations*](https://md.research.elamur.ai/free/en/papers/ramamurthy-2022-reinforcement-learning-natural-language-processing/)
161. [**Abstract Meaning Representation for Multi-Document Summarization**Kexin Liao, Logan Lebanoff, Fei Liu*2018 · 166 citations*](https://md.research.elamur.ai/free/en/papers/liao-2018-abstract-meaning-representation-multi-document/)
162. [**Semantic Noise Matters for Neural Natural Language Generation**Ondřej Dušek, David M. Howcroft, Verena Rieser*2019 · 164 citations*](https://md.research.elamur.ai/free/en/papers/dusek-2019-semantic-noise-matters-neural-natural/)
163. [**DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications**Wei He, Kai Liu, Jing Liu, et al.*2017 · 163 citations*](https://md.research.elamur.ai/free/en/papers/he-2017-dureader-chinese-machine-reading/)
164. [**Long-form factuality in large language models**Jerry Wei, Chengrun Yang, Xinying Song, et al.*2024 · 162 citations*](https://md.research.elamur.ai/free/en/papers/wei-2024-long-form-factuality-large-language/)
165. [**SCROLLS: Standardized CompaRison Over Long Language Sequences**Uri Shaham, Elad Segal, Maor Ivgi, et al.*2022 · 162 citations*](https://md.research.elamur.ai/free/en/papers/shaham-2022-scrolls-standardized-comparison-over-long/)
166. [**Explanations for CommonsenseQA: New Dataset and Models**Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, et al.*2021 · 162 citations*](https://md.research.elamur.ai/free/en/papers/aggarwal-2021-explanations-commonsenseqa-new-dataset-models/)
167. [**ChatGPT as a Factual Inconsistency Evaluator for Text Summarization**Zheheng Luo, Qianqian Xie, Sophia Ananiadou*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-chatgpt-factual-inconsistency-evaluator-text/)
168. [**Orca 2: Teaching Small Language Models How to Reason**Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/)
169. [**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/)
170. [**L-Eval: Instituting Standardized Evaluation for Long Context Language Models**Chenxin An, Shansan Gong, Ming Zhong, et al.*2023 · 159 citations*](https://md.research.elamur.ai/free/en/papers/an-2023-l-eval-instituting-standardized-evaluation/)
171. [**FacTool: Factuality Detection in Generative AI — A Tool Augmented Framework for Multi-Task and Multi-Domain Scenarios**I-Chun Chern, Steffi Chern, Shiqi Chen, et al.*2023 · 158 citations*](https://md.research.elamur.ai/free/en/papers/chern-2023-factool-factuality-detection-generative-ai/)
172. [**ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning**Olga Golovneva, Moya Chen, Spencer Poff, et al.*2022 · 149 citations*](https://md.research.elamur.ai/free/en/papers/golovneva-2022-roscoe-suite-metrics-scoring-step/)
173. [**Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding**Jun Zhang, Jue Wang, Huan Li, et al.*2024 · 146 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-draft-verify-lossless-large-language/)
174. [**MiniLLM: On-Policy Distillation of Large Language Models**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 143 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
175. [**Long-term Recurrent Convolutional Networks for Visual Recognition and Description**Jeff Donahue, Lisa Anne Hendricks, Marcus Rohrbach, et al.*2014 · 142 citations*](https://md.research.elamur.ai/free/en/papers/donahue-2014-long-term-recurrent-convolutional-networks/)
176. [**JudgeLM: Fine-tuned Large Language Models are Scalable Judges**Lianghui Zhu, Xinggang Wang, Xinlong Wang*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-judgelm-fine-tuned-large-language/)
177. [**Paragraph-level Simplification of Medical Texts**Ashwin Devaraj, Iain Marshall, Byron Wallace, et al.*2021 · 139 citations*](https://md.research.elamur.ai/free/en/papers/devaraj-2021-paragraph-level-simplification-medical-texts/)
178. [**Investigating Data Contamination in Modern Benchmarks for Large Language Models**Chunyuan Deng, Yilun Zhao, Xiangru Tang, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/deng-2024-investigating-data-contamination-modern-benchmarks/)
179. [**NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics**Ximing Lu, Sean Welleck, Peter West, et al.*2022 · 136 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-neurologic-esque-decoding-constrained-text/)
180. [**SummScreen: A Dataset for Abstractive Screenplay Summarization**Mingda Chen, Zewei Chu, Sam Wiseman, et al.*2022 · 136 citations*](https://md.research.elamur.ai/free/en/papers/chen-2022-summscreen-dataset-abstractive-screenplay-summarization/)
181. [**SLiC-HF: Sequence Likelihood Calibration with Human Feedback**Yao Zhao, Rishabh Joshi, Tianqi Liu, et al.*2023 · 135 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-slic-hf-sequence-likelihood-calibration/)
182. [**Many-Shot In-Context Learning**Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.*2024 · 134 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/)
183. [**Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models**Pat Verga, Sebastian Hofstatter, Sophia Althammer, et al.*2024 · 134 citations*](https://md.research.elamur.ai/free/en/papers/verga-2024-replacing-judges-juries-evaluating-llm/)
184. [**Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches**Shane Storks, Qiaozi Gao, Joyce Y. Chai*2019 · 134 citations*](https://md.research.elamur.ai/free/en/papers/storks-2019-recent-advances-natural-language-inference/)
185. [**TOFU: A Task of Fictitious Unlearning for LLMs**Pratyush Maini, Zhili Feng, Avi Schwarzschild, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/maini-2024-tofu-task-fictitious-unlearning-llms/)
186. [**Syntax-Guided Controlled Generation of Paraphrases**Ashutosh Kumar, Kabir Ahuja, Raghuram Vadapalli, et al.*2020 · 131 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2020-syntax-guided-controlled-generation-paraphrases/)
187. [**Learning Compact Metrics for MT**Amy Pu, Hyung Won Chung, Ankur Parikh, et al.*2021 · 130 citations*](https://md.research.elamur.ai/free/en/papers/pu-2021-learning-compact-metrics-mt/)
188. [**Retrieval meets Long Context Large Language Models**Peng Xu, Wei Ping, Xianchao Wu, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-retrieval-meets-long-context-large/)
189. [**ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding**Uri Shaham, Maor Ivgi, Avia Efrat, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/shaham-2023-zeroscrolls-zero-shot-benchmark-long/)
190. [**Time Travel in LLMs: Tracing Data Contamination in Large Language Models**Shahriar Golchin, Mihai Surdeanu*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/golchin-2023-time-travel-llms-tracing-data/)
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