---
title: "Who cites Jack W. Rae — page 5 — 13,732 papers of our library"
description: "13,732 papers of the Elamur Research library cite the works of Jack W. Rae, found in their bibliographies · page 5 of 31 · 401–500; the most…"
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---
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# Who cites Jack W. Rae

17,168 citations of their works from 13,732 papers of our library (of the 149,439 whose bibliographies we read) · 3,050 with a page here, the most cited first · page 5 of 31 · 401–500 · [← Jack W. Rae](https://md.research.elamur.ai/free/en/authors/jack-w-rae/)

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401. **[What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://md.research.elamur.ai/free/en/papers/garg-2022-transformers-learn-context-case-study/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shivam Garg, Dimitris Tsipras, Percy Liang, et al.*2022 · 209 citations*
402. **[Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs](https://md.research.elamur.ai/free/en/papers/tong-2024-cambrian-1-fully-open-vision/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shengbang Tong, Ellis Brown, Penghao Wu, et al.*2024 · 208 citations*
403. [**DeepSeek-VL: Towards Real-World Vision-Language Understanding arXiv:2403.05525**Haoyu Lu, Wen Liu, Bo Zhang, et al.*2024 · 208 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-deepseek-vl-real-world/)
404. **[RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models](https://md.research.elamur.ai/free/en/papers/niu-2024-ragtruth-hallucination-corpus-developing-trustworthy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cheng Niu, Yuanhao Wu, Juno Zhu, et al.*2024 · 207 citations*
405. [**LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models arXiv:2305.13711**Yen-Ting Lin, Yun-Nung Chen*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-llm-eval-unified-multi-dimensional/)
406. [**NExT-GPT: Any-to-Any Multimodal LLM arXiv:2309.05519**Shengqiong Wu, Hao Fei, Leigang Qu, et al.*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-next-gpt-any-any-multimodal/)
407. **[BLINK: Multimodal Large Language Models Can See but Not Perceive](https://md.research.elamur.ai/free/en/papers/fu-2024-blink-multimodal-large-language-models/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Xingyu Fu, Yushi Hu, Bangzheng Li, et al.*2024 · 206 citations*
408. **[Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://md.research.elamur.ai/free/en/papers/ovadia-2024-fine-tuning-or-retrieval-comparing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Oded Ovadia, Menachem Brief, Moshik Mishaeli, et al.*2024 · 206 citations*
409. **[Language Model Tokenizers Introduce Unfairness Between Languages](https://md.research.elamur.ai/free/en/papers/petrov-2023-language-model-tokenizers-introduce-unfairness/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Aleksandar Petrov, Emanuele La Malfa, Philip H. S. Torr, et al.*2023 · 206 citations*
410. **[Woodpecker: Hallucination Correction for Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/yin-2023-woodpecker-hallucination-correction-multimodal-large/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Shukang Yin, Chaoyou Fu, Sirui Zhao, et al.*2023 · 206 citations*
411. [**Autoformalization with Large Language Models arXiv:2205.12615**Yuhuai Wu, Albert Q. Jiang, Wenda Li, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/wu-2022-autoformalization-large-language-models/)
412. **[Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset](https://md.research.elamur.ai/free/en/papers/wang-2024-measuring-multimodal-mathematical-reasoning-math/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ke Wang, Junting Pan, Weikang Shi, et al.*2024 · 203 citations*
413. [**Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference arXiv:2406.10774**Jiaming Tang, Yilong Zhao, Kan Zhu, et al.*2024 · 203 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-quest-query-aware-sparsity-efficient/)
414. [**mGPT: Few-Shot Learners Go Multilingual arXiv:2204.07580**Oleh Shliazhko, Alena Fenogenova, Maria Tikhonova, et al.*2022 · 203 citations*](https://md.research.elamur.ai/free/en/papers/shliazhko-2022-mgpt-few-shot-learners-go/)
415. **[How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs](https://md.research.elamur.ai/free/en/papers/zeng-2024-how-johnny-can-persuade-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yi Zeng, Hongpeng Lin, Jingwen Zhang, et al.*2024 · 202 citations*
416. **[MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention](https://md.research.elamur.ai/free/en/papers/jiang-2024-minference-1-0-accelerating-pre/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Huiqiang Jiang, Yucheng Li, Chengruidong Zhang, et al.*2024 · 202 citations*
417. **[Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://md.research.elamur.ai/free/en/papers/yu-2023-large-language-model-attributed-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yue Yu, Yuchen Zhuang, Jieyu Zhang, et al.*2023 · 202 citations*
418. [**Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs arXiv:2310.01801**Suyu Ge, Yunan Zhang, Liyuan Liu, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/ge-2023-model-tells-discard-adaptive-kv/)
419. [**RRHF: Rank Responses to Align Language Models with Human Feedback without tears arXiv:2304.05302**Zheng Yuan, Hongyi Yuan, Chuanqi Tan, et al.*2023 · 201 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-rrhf-rank-responses-align-language/)
420. **[ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search](https://md.research.elamur.ai/free/en/papers/zhang-2024-rest-mcts-llm-self-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Dan Zhang, Sining Zhoubian, Ziniu Hu, et al.*2024 · 200 citations*
421. **[A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 200 citations*
422. [**IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages arXiv:2305.16307**Jay Gala, Pranjal A. Chitale, Raghavan AK, et al.*2023 · 200 citations*](https://md.research.elamur.ai/free/en/papers/gala-2023-indictrans2-towards-high-quality-accessible/)
423. **[A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Xiaowei Huang, Wenjie Ruan, Wei Huang, et al.*2023 · 199 citations*
424. [**MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning arXiv:2310.09478**Jun Chen, Deyao Zhu, Xiaoqian Shen, et al.*2023 · 199 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-minigpt-v2-large-language-model/)
425. **[Improving Passage Retrieval with Zero-Shot Question Generation](https://md.research.elamur.ai/free/en/papers/sachan-2022-improving-passage-retrieval-zero-shot/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Devendra Sachan, Mike Lewis, Mandar Joshi, et al.*2022 · 199 citations*
426. **[MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Text](https://md.research.elamur.ai/free/en/papers/chen-2022-murag-multimodal-retrieval-augmented-generator/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenhu Chen, Hexiang Hu, Xi Chen, et al.*2022 · 199 citations*
427. [**Will we run out of data? Limits of LLM scaling based on human-generated data arXiv:2211.04325**Pablo Villalobos, Anson Ho, Jaime Sevilla, et al.*2022 · 198 citations*](https://md.research.elamur.ai/free/en/papers/villalobos-2022-will-run-out-data-limits/)
428. **[Towards Faithful Model Explanation in NLP: A Survey](https://md.research.elamur.ai/free/en/papers/lyu-2024-faithful-model-explanation-nlp-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Qing Lyu, Marianna Apidianaki, Chris Callison-Burch*2024 · 197 citations*
429. **[OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-obelics-open-web-scale-filtered/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hugo Laurençon, Lucile Saulnier, Leo Tronchon, et al.*2023 · 197 citations*
430. [**DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence arXiv:2406.11931**DeepSeek-AI, Qihao Zhu, Daya Guo, et al.*2024 · 196 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2024-deepseek-coder-v2-breaking-barrier/)
431. [**MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action arXiv:2303.11381**Zhengyuan Yang, Linjie Li, Jianfeng Wang, et al.*2023 · 196 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-mm-react-prompting-chatgpt-multimodal/)
432. [**LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods arXiv:2412.05579**Haitao Li, Qian Dong, Junjie Chen, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llms-judges-comprehensive-survey-llm/)
433. **[Tool Learning with Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/qu-2024-tool-learning-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Changle Qu, Sunhao Dai, Xiaochi Wei, et al.*2024 · 195 citations*
434. **[EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding](https://md.research.elamur.ai/free/en/papers/mangalam-2023-egoschema-diagnostic-benchmark-very-long/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Karttikeya Mangalam, Raiymbek Akshulakov, Jitendra Malik*2023 · 195 citations*
435. **[Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback](https://md.research.elamur.ai/free/en/papers/lai-2023-okapi-instruction-tuned-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Viet Lai, Chien Nguyen, Nghia Ngo, et al.*2023 · 195 citations*
436. [**InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction arXiv:2304.08085**Xiao Wang, Weikang Zhou, Can Zu, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-instructuie-multi-task-instruction-tuning/)
437. [**PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization arXiv:2306.05087**Yidong Wang, Zhuohao Yu, Zhengran Zeng, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-pandalm-automatic-evaluation-benchmark-llm/)
438. **[InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents](https://md.research.elamur.ai/free/en/papers/zhan-2024-injecagent-benchmarking-indirect-prompt-injections/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qiusi Zhan, Zhixiang Liang, Zifan Ying, et al.*2024 · 193 citations*
439. **[LLM in a flash: Efficient Large Language Model Inference with Limited Memory](https://md.research.elamur.ai/free/en/papers/alizadeh-2024-llm-flash-efficient-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Keivan Alizadeh, Seyed Iman Mirzadeh, Dmitry Belenko, et al.*2024 · 193 citations*
440. [**RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval arXiv:2401.18059**Parth Sarthi, Salman Abdullah, Aditi Tuli, et al.*2024 · 193 citations*](https://md.research.elamur.ai/free/en/papers/sarthi-2024-raptor-recursive-abstractive-processing-tree/)
441. **[GEMBA-MQM: Detecting Translation Quality Error Spans with GPT-4](https://md.research.elamur.ai/free/en/papers/kocmi-2023-gemba-mqm-detecting-translation-quality/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Tom Kocmi, Christian Federmann*2023 · 192 citations*
442. [**Multilingual Jailbreak Challenges in Large Language Models arXiv:2310.06474**Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, et al.*2023 · 192 citations*](https://md.research.elamur.ai/free/en/papers/deng-2023-multilingual-jailbreak-challenges-large-language/)
443. **[DataComp-LM: In search of the next generation of training sets for language models](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 191 citations*
444. **[WebVoyager: Building an End-to-End Web Agent with Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/he-2024-webvoyager-building-end-end-web/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hongliang He, Wenlin Yao, Kaixin Ma, et al.*2024 · 191 citations*
445. **[On the Planning Abilities of Large Language Models : A Critical Investigation](https://md.research.elamur.ai/free/en/papers/valmeekam-2023-planning-abilities-large-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, et al.*2023 · 191 citations*
446. **[Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.*2022 · 190 citations*
447. **[GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://md.research.elamur.ai/free/en/papers/zaratiana-2024-gliner-generalist-model-named-entity/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Urchade Zaratiana, Nadi Tomeh, Pierre Holat, et al.*2024 · 189 citations*
448. [**The Prompt Report: A Systematic Survey of Prompt Engineering Techniques arXiv:2406.06608**Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.*2024 · 189 citations*](https://md.research.elamur.ai/free/en/papers/schulhoff-2024-prompt-report-systematic-survey-prompt/)
449. [**Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning arXiv:2306.14565**Fuxiao Liu, Kevin Lin, Linjie Li, et al.*2023 · 189 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-mitigating-hallucination-large-multi-modal/)
450. [**SGPT: GPT Sentence Embeddings for Semantic Search arXiv:2202.08904**Niklas Muennighoff*2022 · 189 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-sgpt-gpt-sentence-embeddings-semantic/)
451. [**Eliciting Latent Predictions from Transformers with the Tuned Lens arXiv:2303.08112**Nora Belrose, Igor Ostrovsky, Lev McKinney, et al.*2023 · 188 citations*](https://md.research.elamur.ai/free/en/papers/belrose-2023-eliciting-latent-predictions-transformers-tuned/)
452. [**LawBench: Benchmarking Legal Knowledge of Large Language Models arXiv:2309.16289**Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, et al.*2023 · 188 citations*](https://md.research.elamur.ai/free/en/papers/fei-2023-lawbench-benchmarking-legal-knowledge-large/)
453. **[Preference Ranking Optimization for Human Alignment](https://md.research.elamur.ai/free/en/papers/song-2023-preference-ranking-optimization-human-alignment/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Feifan Song, Bowen Yu, Minghao Li, et al.*2023 · 188 citations*
454. [**RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models arXiv:2310.00746**Zekun Moore Wang, Zhongyuan Peng, Haoran Que, et al.*2023 · 187 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-rolellm-benchmarking-eliciting-enhancing-role/)
455. [**Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier arXiv:2412.04261**John Dang, Shivalika Singh, Daniel D'souza, et al.*2024 · 184 citations*](https://md.research.elamur.ai/free/en/papers/dang-2024-aya-expanse-combining-research-breakthroughs/)
456. [**CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer arXiv:2408.06072**Zhuoyi Yang, Jiayan Teng, Wendi Zheng, et al.*2024 · 184 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-cogvideox-text-video-diffusion/)
457. **[Towards Building Multilingual Language Model for Medicine](https://md.research.elamur.ai/free/en/papers/qiu-2024-towards-building-multilingual-language-model/) [Nature Communications](https://md.research.elamur.ai/free/en/venues/nature-communications/)**Pengcheng Qiu, Chaoyi Wu, Xiaoman Zhang, et al.*2024 · 184 citations*
458. **[VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks](https://md.research.elamur.ai/free/en/papers/koh-2024-visualwebarena-evaluating-multimodal-agents-realistic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jing Yu Koh, Robert Lo, Lawrence Jang, et al.*2024 · 184 citations*
459. [**Effective Long-Context Scaling of Foundation Models arXiv:2309.16039**Wenhan Xiong, Jingyu Liu, Igor Molybog, et al.*2023 · 184 citations*](https://md.research.elamur.ai/free/en/papers/xiong-2023-effective-long-context-scaling-foundation/)
460. [**Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models arXiv:2311.07919**Yunfei Chu, Jin Xu, Xiaohuan Zhou, et al.*2023 · 184 citations*](https://md.research.elamur.ai/free/en/papers/chu-2023-qwen-audio-advancing-universal/)
461. [**Low-Resource Languages Jailbreak GPT-4 arXiv:2310.02446**Zheng-Xin Yong, Cristina Menghini, Stephen H. Bach*2023 · 183 citations*](https://md.research.elamur.ai/free/en/papers/yong-2023-low-resource-languages-jailbreak-gpt/)
462. **[Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation](https://md.research.elamur.ai/free/en/papers/singh-2025-global-mmlu-understanding-addressing-cultural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shivalika Singh, Angelika Romanou, Clémentine Fourrier, et al.*2025 · 182 citations*
463. **[Knowledge Conflicts for LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/xu-2024-knowledge-conflicts-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Rongwu Xu, Zehan Qi, Zhijiang Guo, et al.*2024 · 182 citations*
464. **[Thinking in Space: How Multimodal Large Language Models See, Remember, and Recall Spaces](https://md.research.elamur.ai/free/en/papers/yang-2024-thinking-space-multimodal-large-language/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Jihan Yang, Shusheng Yang, Anjali W. Gupta, et al.*2024 · 182 citations*
465. **[Adapting Language Models to Compress Contexts](https://md.research.elamur.ai/free/en/papers/chevalier-2023-adapting-language-models-compress-contexts/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alexis Chevalier, Alexander Wettig, Anirudh Ajith, et al.*2023 · 182 citations*
466. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
467. **[Large Language Models are few(1)-shot Table Reasoners](https://md.research.elamur.ai/free/en/papers/chen-2023-large-language-models-are-few/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenhu Chen*2023 · 182 citations*
468. **[ToolQA: A Dataset for LLM Question Answering with External Tools](https://md.research.elamur.ai/free/en/papers/zhuang-2023-toolqa-dataset-llm-question-answering/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuchen Zhuang, Yue Yu, Kuan Wang, et al.*2023 · 182 citations*
469. [**SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language Model arXiv:2502.02737**Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al.*2025 · 181 citations*](https://md.research.elamur.ai/free/en/papers/allal-2025-smollm2-smol-goes-big-data/)
470. **[CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages](https://md.research.elamur.ai/free/en/papers/nguyen-2023-culturax-cleaned-enormous-multilingual-dataset/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, et al.*2023 · 181 citations*
471. [**ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases arXiv:2306.05301**Qiaoyu Tang, Ziliang Deng, Hongyu Lin, et al.*2023 · 180 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-toolalpaca-generalized-tool-learning-language/)
472. [**InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency arXiv:2508.18265**Weiyun Wang, Zhangwei Gao, Lixin Gu, et al.*2025 · 179 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-internvl3-5-advancing-open/)
473. **[Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://md.research.elamur.ai/free/en/papers/tseng-2024-two-tales-persona-llms-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao, et al.*2024 · 179 citations*
474. **[On the Risk of Misinformation Pollution with Large Language Models](https://md.research.elamur.ai/free/en/papers/pan-2023-risk-misinformation-pollution-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yikang Pan, Liangming Pan, Wenhu Chen, et al.*2023 · 179 citations*
475. **[A-MEM: Agentic Memory for LLM Agents](https://md.research.elamur.ai/free/en/papers/xu-2025-mem-agentic-memory-llm-agents/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Wujiang Xu, Zujie Liang, Kai Mei, et al.*2025 · 178 citations*
476. **[Large Language Diffusion Models](https://md.research.elamur.ai/free/en/papers/nie-2025-large-language-diffusion-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shen Nie, Fengqi Zhu, Zebin You, et al.*2025 · 178 citations*
477. **[Findings of the WMT24 General Machine Translation Shared Task: The LLM Era Is Here but MT Is Not Solved Yet](https://md.research.elamur.ai/free/en/papers/kocmi-2024-findings-wmt24-general-machine-translation/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Tom Kocmi, Eleftherios Avramidis, Rachel Bawden, et al.*2024 · 178 citations*
478. **[Learning to Compress Prompts with Gist Tokens](https://md.research.elamur.ai/free/en/papers/mu-2023-learning-compress-prompts-gist-tokens/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jesse Mu, Xiang Lisa Li, Noah Goodman*2023 · 178 citations*
479. [**What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning arXiv:2312.15685**Wei Liu, Weihao Zeng, Keqing He, et al.*2023 · 178 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-makes-good-data-alignment-comprehensive/)
480. **[Do Llamas Work in English? On the Latent Language of Multilingual Transformers](https://md.research.elamur.ai/free/en/papers/wendler-2024-do-llamas-work-english-latent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chris Wendler, Veniamin Veselovsky, Giovanni Monea, et al.*2024 · 177 citations*
481. [**What Artificial Neural Networks Can Tell Us About Human Language Acquisition arXiv:2208.07998**Alex Warstadt, Samuel R. Bowman*2022 · 177 citations*](https://md.research.elamur.ai/free/en/papers/warstadt-2022-artificial-neural-networks-tell-us/)
482. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
483. [**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/)
484. **[Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://md.research.elamur.ai/free/en/papers/balloccu-2024-leak-cheat-repeat-data-contamination/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, et al.*2024 · 174 citations*
485. [**Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents arXiv:2302.01560**Zihao Wang, Shaofei Cai, Guanzhou Chen, et al.*2023 · 174 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-describe-explain-plan-select-interactive/)
486. **[Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models](https://md.research.elamur.ai/free/en/papers/ahia-2023-do-all-languages-cost-same/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2023 · 174 citations*
487. [**OpenChat: Advancing Open-source Language Models with Mixed-Quality Data arXiv:2309.11235**Guan Wang, Sijie Cheng, Xianyuan Zhan, et al.*2023 · 174 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-openchat-advancing-open-source-language/)
488. [**AI Alignment: A Comprehensive Survey arXiv:2310.19852**Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-ai-alignment-comprehensive-survey/)
489. [**Aya 23: Open Weight Releases to Further Multilingual Progress arXiv:2405.15032**Viraat Aryabumi, John Dang, Dwarak Talupuru, et al.*2024 · 172 citations*](https://md.research.elamur.ai/free/en/papers/aryabumi-2024-aya-23-open-weight-releases/)
490. [**Generative Representational Instruction Tuning arXiv:2402.09906**Niklas Muennighoff, Hongjin Su, Liang Wang, et al.*2024 · 172 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/)
491. **[LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Theo X. Olausson, Alex Gu, Benjamin Lipkin, et al.*2023 · 172 citations*
492. [**Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision arXiv:2312.09390**Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.*2023 · 172 citations*](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/)
493. [**Scaling Synthetic Data Creation with 1,000,000,000 Personas arXiv:2406.20094**Tao Ge, Xin Chan, Xiaoyang Wang, et al.*2024 · 171 citations*](https://md.research.elamur.ai/free/en/papers/ge-2024-scaling-synthetic-data-creation-1/)
494. **[TopicGPT: A Prompt-based Topic Modeling Framework](https://md.research.elamur.ai/free/en/papers/pham-2024-topicgpt-prompt-based-topic-modeling/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chau Minh Pham, Alexander Hoyle, Simeng Sun, et al.*2024 · 171 citations*
495. [**AudioPaLM: A Large Language Model That Can Speak and Listen arXiv:2306.12925**Paul K. Rubenstein, Chulayuth Asawaroengchai, Duc Dung Nguyen, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/rubenstein-2023-audiopalm-large-language-model-that/)
496. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity arXiv:2310.07521**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
497. **[The Troubling Emergence of Hallucination in Large Language Models - An Extensive Definition, Quantification, and Prescriptive Remediations](https://md.research.elamur.ai/free/en/papers/rawte-2023-troubling-emergence-hallucination-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, et al.*2023 · 171 citations*
498. [**Capabilities of Gemini Models in Medicine arXiv:2404.18416**Khaled Saab, Tao Tu, Wei-Hung Weng, et al.*2024 · 170 citations*](https://md.research.elamur.ai/free/en/papers/saab-2024-capabilities-gemini-models-medicine/)
499. [**SqueezeLLM: Dense-and-Sparse Quantization arXiv:2306.07629**Sehoon Kim, Coleman Hooper, Amir Gholami, et al.*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-squeezellm-dense-sparse-quantization/)
500. [**Binding Language Models in Symbolic Languages arXiv:2210.02875**Zhoujun Cheng, Tianbao Xie, Peng Shi, et al.*2022 · 170 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2022-binding-language-models-symbolic-languages/)

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