---
title: "Who cites Mark I-Cheng Chen — page 7 — 16,629 citing papers"
description: "16,629 papers of the Elamur Research library cite the works of Mark I-Cheng Chen, found in their bibliographies · page 7 of 33 · 601–700; the…"
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---
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# Who cites Mark I-Cheng Chen

20,687 citations of their works from 16,629 papers of our library (of the 149,439 whose bibliographies we read) · 3,273 with a page here, the most cited first · page 7 of 33 · 601–700 · [← Mark I-Cheng Chen](https://md.research.elamur.ai/free/en/authors/mark-i-cheng-chen/)

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601. **[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*
602. **[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*
603. [**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/)
604. [**Refusal in Language Models Is Mediated by a Single Direction arXiv:2406.11717**Andy Arditi, Oscar Obeso, Aaquib Syed, et al.*2024 · 196 citations*](https://md.research.elamur.ai/free/en/papers/arditi-2024-refusal-language-models-mediated-single/)
605. **[High-Fidelity Audio Compression with Improved RVQGAN](https://md.research.elamur.ai/free/en/papers/kumar-2023-high-fidelity-audio-compression-improved/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rithesh Kumar, Prem Seetharaman, Alejandro Luebs, et al.*2023 · 196 citations*
606. [**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/)
607. **[The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://md.research.elamur.ai/free/en/papers/ye-2022-unreliability-explanations-few-shot-prompting/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xi Ye, Greg Durrett*2022 · 196 citations*
608. [**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/)
609. **[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*
610. **[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*
611. [**Making Retrieval-Augmented Language Models Robust to Irrelevant Context arXiv:2310.01558**Ori Yoran, Tomer Wolfson, Ori Ram, et al.*2023 · 195 citations*](https://md.research.elamur.ai/free/en/papers/yoran-2023-making-retrieval-augmented-language-models/)
612. [**OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models arXiv:2308.13137**Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang, et al.*2023 · 195 citations*](https://md.research.elamur.ai/free/en/papers/shao-2023-omniquant-omnidirectionally-calibrated-quantization-large/)
613. [**Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collections arXiv:2104.04670**Ruiqi Zhong, Kristy Lee, Zheng Zhang, et al.*2021 · 195 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2021-adapting-language-models-zero-shot/)
614. [**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/)
615. [**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/)
616. **[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*
617. **[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*
618. [**Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs arXiv:2412.21187**Xingyu Chen, Jiahao Xu, Tian Liang, et al.*2024 · 192 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-think-that-much-2-3/)
619. **[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*
620. **[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*
621. **[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*
622. [**CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers arXiv:2205.15868**Wenyi Hong, Ming Ding, Wendi Zheng, et al.*2022 · 191 citations*](https://md.research.elamur.ai/free/en/papers/hong-2022-cogvideo-large-scale-pretraining/)
623. **[Challenges in Automated Debiasing for Toxic Language Detection](https://md.research.elamur.ai/free/en/papers/zhou-2021-challenges-automated-debiasing-toxic-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, et al.*2021 · 191 citations*
624. **[When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/muller-2021-when-being-unseen-mbert-is/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Benjamin Muller, Antonios Anastasopoulos, Benoît Sagot, et al.*2021 · 191 citations*
625. [**CodeS: Towards Building Open-source Language Models for Text-to-SQL arXiv:2402.16347**Haoyang Li, Jing Zhang, Hanbing Liu, et al.*2024 · 190 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-codes-towards-building-open-source/)
626. **[Towards Making the Most of ChatGPT for Machine Translation](https://md.research.elamur.ai/free/en/papers/peng-2023-making-most-chatgpt-machine-translation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Keqin Peng, Liang Ding, Qihuang Zhong, et al.*2023 · 190 citations*
627. **[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*
628. **[Compressing Large-Scale Transformer-Based Models: A Case Study on BERT](https://md.research.elamur.ai/free/en/papers/ganesh-2021-compressing-large-scale-transformer-based/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Prakhar Ganesh, Yao Chen, Xin Lou, et al.*2021 · 190 citations*
629. **[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*
630. [**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/)
631. [**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/)
632. [**DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models arXiv:2210.08933**Shansan Gong, Mukai Li, Jiangtao Feng, et al.*2022 · 189 citations*](https://md.research.elamur.ai/free/en/papers/gong-2022-diffuseq-sequence-sequence-text-generation/)
633. **[Packed Levitated Marker for Entity and Relation Extraction](https://md.research.elamur.ai/free/en/papers/ye-2022-packed-levitated-marker-entity-relation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Deming Ye, Yankai Lin, Peng Li, et al.*2022 · 189 citations*
634. [**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/)
635. **[Improving and Simplifying Pattern Exploiting Training](https://md.research.elamur.ai/free/en/papers/tam-2021-improving-simplifying-pattern-exploiting-training/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Derek Tam, Rakesh R. Menon, Mohit Bansal, et al.*2021 · 189 citations*
636. [**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/)
637. [**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/)
638. **[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*
639. [**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/)
640. [**Adaptive Machine Translation with Large Language Models arXiv:2301.13294**Yasmin Moslem, Rejwanul Haque, John D. Kelleher, et al.*2023 · 186 citations*](https://md.research.elamur.ai/free/en/papers/moslem-2023-adaptive-machine-translation-large-language/)
641. **[Deja Vu: Contextual Sparsity for Efficient LLMs at Inference Time](https://md.research.elamur.ai/free/en/papers/liu-2023-deja-vu-contextual-sparsity-efficient/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Zichang Liu, Jue Wang, Tri Dao, et al.*2023 · 185 citations*
642. **[SemEval-2023 Task 10: Explainable Detection of Online Sexism](https://md.research.elamur.ai/free/en/papers/kirk-2023-semeval-2023-task-10-explainable/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Hannah Kirk, Wenjie Yin, Bertie Vidgen, et al.*2023 · 185 citations*
643. [**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/)
644. **[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*
645. **[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*
646. [**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/)
647. **[GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models](https://md.research.elamur.ai/free/en/papers/prasad-2023-grips-gradient-free-edit-based/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Archiki Prasad, Peter Hase, Xiang Zhou, et al.*2023 · 184 citations*
648. [**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/)
649. **[Re2G: Retrieve, Rerank, Generate](https://md.research.elamur.ai/free/en/papers/glass-2022-re2g-retrieve-rerank-generate/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, et al.*2022 · 184 citations*
650. **[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*
651. **[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*
652. **[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*
653. [**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/)
654. **[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*
655. [**The False Promise of Imitating Proprietary LLMs arXiv:2305.15717**Arnav Gudibande, Eric Wallace, Charlie Snell, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/gudibande-2023-false-promise-imitating-proprietary-llms/)
656. **[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*
657. **[Template-free Prompt Tuning for Few-shot NER](https://md.research.elamur.ai/free/en/papers/ma-2022-template-free-prompt-tuning-few/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ruotian Ma, Xin Zhou, Tao Gui, et al.*2022 · 182 citations*
658. [**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/)
659. **[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*
660. **[Prompt-learning for Fine-grained Entity Typing](https://md.research.elamur.ai/free/en/papers/ding-2022-prompt-learning-fine-grained-entity/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ning Ding, Yulin Chen, Xu Han, et al.*2022 · 181 citations*
661. **[RealTime QA: What's the Answer Right Now?](https://md.research.elamur.ai/free/en/papers/kasai-2022-realtime-qa-s-answer-right/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, et al.*2022 · 181 citations*
662. [**8-bit Optimizers via Block-wise Quantization arXiv:2110.02861**Tim Dettmers, Mike Lewis, Sam Shleifer, et al.*2021 · 181 citations*](https://md.research.elamur.ai/free/en/papers/dettmers-2021-8-bit-optimizers-block-wise/)
663. **[An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels](https://md.research.elamur.ai/free/en/papers/sorensen-2022-information-theoretic-approach-prompt-engineering/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Taylor Sorensen, Joshua Robinson, Christopher Rytting, et al.*2022 · 180 citations*
664. **[Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again](https://md.research.elamur.ai/free/en/papers/gutierrez-2022-thinking-about-gpt-3-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bernal Jimenez Gutierrez, Nikolas McNeal, Clayton Washington, et al.*2022 · 180 citations*
665. **[Multilingual LAMA: Investigating Knowledge in Multilingual Pretrained Language Models](https://md.research.elamur.ai/free/en/papers/kassner-2021-multilingual-lama-investigating-knowledge-multilingual/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Nora Kassner, Philipp Dufter, Hinrich Schütze*2021 · 180 citations*
666. [**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/)
667. **[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*
668. **[LILA: A Unified Benchmark for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/mishra-2022-lila-unified-benchmark-mathematical-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Swaroop Mishra, Matthew Finlayson, Pan Lu, et al.*2022 · 179 citations*
669. [**KernelBench: Can LLMs Write Efficient GPU Kernels? arXiv:2502.10517**Anne Ouyang, Simon Guo, Simran Arora, et al.*2025 · 178 citations*](https://md.research.elamur.ai/free/en/papers/ouyang-2025-kernelbench-llms-write-efficient-gpu/)
670. **[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*
671. [**LESS: Selecting Influential Data for Targeted Instruction Tuning arXiv:2402.04333**Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, et al.*2024 · 178 citations*](https://md.research.elamur.ai/free/en/papers/xia-2024-less-selecting-influential-data-targeted/)
672. **[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*
673. [**Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs arXiv:2210.12283**Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, et al.*2022 · 178 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2022-draft-sketch-prove-guiding-formal/)
674. [**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/)
675. [**BASE Layers: Simplifying Training of Large, Sparse Models arXiv:2103.16716**Mike Lewis, Shruti Bhosale, Tim Dettmers, et al.*2021 · 177 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2021-base-layers-simplifying-training-large/)
676. **[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*
677. [**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/)
678. [**Aligning Large Multimodal Models with Factually Augmented RLHF arXiv:2309.14525**Zhiqing Sun, Sheng Shen, Shengcao Cao, et al.*2023 · 176 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-aligning-large-multimodal-models-factually/)
679. [**On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective arXiv:2302.12095**Jindong Wang, Xixu Hu, Wenxin Hou, et al.*2023 · 176 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-robustness-chatgpt-adversarial-out-distribution/)
680. [**Baldur: Whole-Proof Generation and Repair with Large Language Models arXiv:2303.04910**Emily First, Markus N. Rabe, Talia Ringer, et al.*2023 · 175 citations*](https://md.research.elamur.ai/free/en/papers/first-2023-baldur-whole-proof-generation-repair/)
681. **[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*
682. [**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/)
683. **[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*
684. [**Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution arXiv:2309.16797**Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, et al.*2023 · 174 citations*](https://md.research.elamur.ai/free/en/papers/fernando-2023-promptbreeder-self-referential-self-improvement/)
685. [**PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts arXiv:2202.01279**Stephen H. Bach, Victor Sanh, Zheng-Xin Yong, et al.*2022 · 174 citations*](https://md.research.elamur.ai/free/en/papers/bach-2022-promptsource-integrated-development-environment-repository/)
686. [**AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning arXiv:2303.10512**Qingru Zhang, Minshuo Chen, Alexander Bukharin, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-adalora-adaptive-budget-allocation-parameter/)
687. [**Diffusion Policy: Visuomotor Policy Learning via Action Diffusion arXiv:2303.04137**Cheng Chi, Zhenjia Xu, Siyuan Feng, et al.*2023 · 173 citations*](https://md.research.elamur.ai/free/en/papers/chi-2023-diffusion-policy-visuomotor-policy-learning/)
688. [**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/)
689. [**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/)
690. **[DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://md.research.elamur.ai/free/en/papers/su-2023-detectllm-leveraging-log-rank-information/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinyan Su, Terry Zhuo, Di Wang, et al.*2023 · 172 citations*
691. **[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*
692. [**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/)
693. **[ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering](https://md.research.elamur.ai/free/en/papers/chen-2022-convfinqa-exploring-chain-numerical-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhiyu Chen, Shiyang Li, Charese Smiley, et al.*2022 · 172 citations*
694. [**Deduplicating Training Data Mitigates Privacy Risks in Language Models arXiv:2202.06539**Nikhil Kandpal, Eric Wallace, Colin Raffel*2022 · 172 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-deduplicating-training-data-mitigates-privacy/)
695. [**Generating Sequences by Learning to Self-Correct arXiv:2211.00053**Sean Welleck, Ximing Lu, Peter West, et al.*2022 · 172 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2022-generating-sequences-learning-self-correct/)
696. **[Self-training Improves Pre-training for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/du-2021-self-training-improves-pre-training/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jingfei Du, Edouard Grave, Beliz Gunel, et al.*2021 · 172 citations*
697. [**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/)
698. [**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/)
699. **[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*
700. [**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/)

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