---
title: "Who cites Gaurav Mishra — page 7 — 7,760 papers of our library"
description: "7,760 papers of the Elamur Research library cite the works of Gaurav Mishra, found in their bibliographies · page 7 of 19 · 601–700; the most…"
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---
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# Who cites Gaurav Mishra

9,492 citations of their works from 7,760 papers of our library (of the 149,439 whose bibliographies we read) · 1,843 with a page here, the most cited first · page 7 of 19 · 601–700 · [← Gaurav Mishra](https://md.research.elamur.ai/free/en/authors/gaurav-mishra/)

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601. [**RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation arXiv:2310.04408**Fangyuan Xu, Weijia Shi, Eunsol Choi*2023 · 115 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-recomp-improving-retrieval-augmented-lms/)
602. [**Break the Sequential Dependency of LLM Inference Using Lookahead Decoding arXiv:2402.02057**Yichao Fu, Peter Bailis, Ion Stoica, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/fu-2024-break-sequential-dependency-llm-inference/)
603. [**GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction arXiv:2310.03668**Oscar Sainz, Iker Garcia-Ferrero, Rodrigo Agerri, et al.*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/sainz-2023-gollie-annotation-guidelines-improve-zero/)
604. **[Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations](https://md.research.elamur.ai/free/en/papers/yoo-2022-ground-truth-labels-matter-deeper/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kang Min Yoo, Junyeob Kim, Hyuhng Joon Kim, et al.*2022 · 114 citations*
605. [**ALLaM: Large Language Models for Arabic and English arXiv:2407.15390**M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/bari-2024-allam-large-language-models-arabic/)
606. **[ExpertQA: Expert-Curated Questions and Attributed Answers](https://md.research.elamur.ai/free/en/papers/malaviya-2024-expertqa-expert-curated-questions-attributed/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chaitanya Malaviya, Subin Lee, Sihao Chen, et al.*2024 · 113 citations*
607. **[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*
608. [**Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling arXiv:2501.17811**Xiaokang Chen, Zhiyu Wu, Xingchao Liu, et al.*2025 · 112 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-janus-pro-unified-multimodal/)
609. **[AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension](https://md.research.elamur.ai/free/en/papers/yang-2024-air-bench-benchmarking-large-audio/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qian Yang, Jin Xu, Wenrui Liu, et al.*2024 · 112 citations*
610. [**DistiLLM: Towards Streamlined Distillation for Large Language Models arXiv:2402.03898**Jongwoo Ko, Sungnyun Kim, Tianyi Chen, et al.*2024 · 112 citations*](https://md.research.elamur.ai/free/en/papers/ko-2024-distillm-towards-streamlined-distillation-large/)
611. **[Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation](https://md.research.elamur.ai/free/en/papers/wu-2024-janus-decoupling-visual-encoding/) [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/)**Chengyue Wu, Xiaokang Chen, Zhiyu Wu, et al.*2024 · 112 citations*
612. [**Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs arXiv:2410.18451**Chris Yuhao Liu, Liang Zeng, Jiacai Liu, et al.*2024 · 112 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-skywork-reward-bag-tricks/)
613. [**Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sources arXiv:2305.13269**Xingxuan Li, Ruochen Zhao, Yew Ken Chia, et al.*2023 · 112 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-chain-knowledge-grounding-large-language/)
614. **[Do Androids Laugh at Electric Sheep? Humor “Understanding” Benchmarks from The New Yorker Caption Contest](https://md.research.elamur.ai/free/en/papers/hessel-2023-do-androids-laugh-electric-sheep/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jack Hessel, Ana Marasovic, Jena D. Hwang, et al.*2023 · 112 citations*
615. [**Don't Make Your LLM an Evaluation Benchmark Cheater arXiv:2311.01964**Kun Zhou, Yutao Zhu, Zhipeng Chen, et al.*2023 · 112 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-don-t-make-llm-evaluation/)
616. [**Orca-Math: Unlocking the potential of SLMs in Grade School Math arXiv:2402.14830**Arindam Mitra, Hamed Khanpour, Corby Rosset, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2024-orca-math-unlocking-potential-slms/)
617. [**ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models arXiv:2404.07738**Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/baek-2024-researchagent-iterative-research-idea/)
618. [**RLHF Workflow: From Reward Modeling to Online RLHF arXiv:2405.07863**Hanze Dong, Wei Xiong, Bo Pang, et al.*2024 · 111 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-rlhf-workflow-reward-modeling-online/)
619. **[Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions?](https://md.research.elamur.ai/free/en/papers/chen-2023-can-pre-trained-vision-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yang Chen, Hexiang Hu, Yi Luan, et al.*2023 · 111 citations*
620. [**Efficient Large Language Models: A Survey arXiv:2312.03863**Zhongwei Wan, Xin Wang, Che Liu, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-efficient-large-language-models-survey/)
621. **[GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP](https://md.research.elamur.ai/free/en/papers/khondaker-2023-gptaraeval-comprehensive-evaluation-chatgpt-arabic/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Md Tawkat Islam Khondaker, Abdul Waheed, El Moatez Billah Nagoudi, et al.*2023 · 110 citations*
622. [**Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification arXiv:2308.07921**Aojun Zhou, Ke Wang, Zimu Lu, et al.*2023 · 110 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-solving-challenging-math-word-problems/)
623. **[SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks](https://md.research.elamur.ai/free/en/papers/lin-2023-swiftsage-generative-agent-fast-slow/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Bill Yuchen Lin, Yicheng Fu, Karina Yang, et al.*2023 · 110 citations*
624. **[Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems](https://md.research.elamur.ai/free/en/papers/miao-2023-towards-efficient-generative-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, et al.*2023 · 110 citations*
625. **[We're Afraid Language Models Aren't Modeling Ambiguity](https://md.research.elamur.ai/free/en/papers/liu-2023-we-re-afraid-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alisa Liu, Zhaofeng Wu, Julian Michael, et al.*2023 · 110 citations*
626. [**Better Zero-Shot Reasoning with Role-Play Prompting arXiv:2308.07702**Aobo Kong, Shiwan Zhao, Hao Chen, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/kong-2023-better-zero-shot-reasoning-role/)
627. **[CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors](https://md.research.elamur.ai/free/en/papers/li-2023-codeie-large-code-generation-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peng Li, Tianxiang Sun, Qiong Tang, et al.*2023 · 109 citations*
628. [**Evaluating the Performance of Large Language Models on GAOKAO Benchmark arXiv:2305.12474**Xiaotian Zhang, Chunyang Li, Yi Zong, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-evaluating-performance-large-language/)
629. [**PaperQA: Retrieval-Augmented Generative Agent for Scientific Research arXiv:2312.07559**Jakub Lála, Odhran O'Donoghue, Aleksandar Shtedritski, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/lala-2023-paperqa-retrieval-augmented-generative/)
630. **[Leveraging GPT-4 for Automatic Translation Post-Editing](https://md.research.elamur.ai/free/en/papers/raunak-2023-leveraging-gpt-4-automatic-translation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Vikas Raunak, Amr Sharaf, Yiren Wang, et al.*2023 · 108 citations*
631. [**LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Composition arXiv:2307.13269**Chengsong Huang, Qian Liu, Bill Yuchen Lin, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-lorahub-efficient-cross-task-generalization/)
632. [**Nemotron-4 340B Technical Report arXiv:2406.11704**Nvidia: Bo Adler, Niket Agarwal, Ashwath Aithal, et al.*2024 · 107 citations*](https://md.research.elamur.ai/free/en/papers/adler-2024-nemotron-4-340b-technical-report/)
633. **[Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?](https://md.research.elamur.ai/free/en/papers/wang-2024-rethinking-bounds-llm-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qineng Wang, Zihao Wang, Ying Su, et al.*2024 · 107 citations*
634. **[Multi-Step Reasoning with Large Language Models, a Survey](https://md.research.elamur.ai/free/en/papers/plaat-2024-multi-step-reasoning-large-language/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Aske Plaat, Annie Wong, Suzan Verberne, et al.*2024 · 106 citations*
635. **[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*
636. **[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*
637. **[TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models](https://md.research.elamur.ai/free/en/papers/gekhman-2023-trueteacher-learning-factual-consistency-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zorik Gekhman, Jonathan Herzig, Roee Aharoni, et al.*2023 · 106 citations*
638. **[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*
639. [**Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understanding arXiv:2401.04398**Zilong Wang, Hao Zhang, Chun-Liang Li, et al.*2024 · 105 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-chain-table-evolving-tables-reasoning/)
640. [**Cost-Efficient Large Language Model Serving for Multi-turn Conversations with CachedAttention USENIX ATC 2024**Bin Gao, Zhuomin He, Puru Sharma, et al.*2024 · 105 citations*](https://md.research.elamur.ai/free/en/papers/gao-2024-cost-efficient-large-language-model/)
641. **[Disentangling Length from Quality in Direct Preference Optimization](https://md.research.elamur.ai/free/en/papers/park-2024-disentangling-length-quality-direct-preference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ryan Park, Rafael Rafailov, Stefano Ermon, et al.*2024 · 105 citations*
642. [**WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild arXiv:2406.04770**Bill Yuchen Lin, Yuntian Deng, Khyathi Chandu, et al.*2024 · 105 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-wildbench-benchmarking-llms-challenging-tasks/)
643. [**Inverse Scaling: When Bigger Isn't Better arXiv:2306.09479 · Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQF**Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
644. [**LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions arXiv:2304.14402**Minghao Wu, Abdul Waheed, Chiyu Zhang, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-lamini-lm-diverse-herd-distilled/)
645. [**Statistical Rejection Sampling Improves Preference Optimization arXiv:2309.06657**Tianqi Liu, Yao Zhao, Rishabh Joshi, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-statistical-rejection-sampling-improves-preference/)
646. **[Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence](https://md.research.elamur.ai/free/en/papers/chen-2022-rich-knowledge-sources-bring-complex/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hung-Ting Chen, Michael Zhang, Eunsol Choi*2022 · 105 citations*
647. [**REINFORCE++: Stabilizing Critic-Free Policy Optimization with Global Advantage Normalization arXiv:2501.03262**Jian Hu, Jason Klein Liu, Haotian Xu, et al.*2025 · 104 citations*](https://md.research.elamur.ai/free/en/papers/hu-2025-reinforce-stabilizing-critic-free-policy/)
648. **[Chain of Agents: Large Language Models Collaborating on Long-Context Tasks](https://md.research.elamur.ai/free/en/papers/zhang-2024-chain-agents-large-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yusen Zhang, Ruoxi Sun, Yanfei Chen, et al.*2024 · 104 citations*
649. [**A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity arXiv:2305.13169**Shayne Longpre, Gregory Yauney, Emily Reif, et al.*2023 · 104 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-pretrainer-s-guide-training-data/)
650. **[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*
651. [**MMTEB: Massive Multilingual Text Embedding Benchmark arXiv:2502.13595**Kenneth Enevoldsen, Isaac Chung, Imene Kerboua, et al.*2025 · 103 citations*](https://md.research.elamur.ai/free/en/papers/enevoldsen-2025-mmteb-massive-multilingual-text-embedding/)
652. **[Can we trust the evaluation on ChatGPT?](https://md.research.elamur.ai/free/en/papers/aiyappa-2023-can-we-trust-evaluation-chatgpt/) [Workshop on Trustworthy NLP](https://md.research.elamur.ai/free/en/venues/trustnlp/)**Rachith Aiyappa, Jisun An, Haewoon Kwak, et al.*2023 · 103 citations*
653. [**PolyLM: An Open Source Polyglot Large Language Model arXiv:2307.06018**Xiangpeng Wei, Haoran Wei, Huan Lin, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-polylm-open-source-polyglot-large/)
654. [**Rethinking Benchmark and Contamination for Language Models with Rephrased Samples arXiv:2311.04850**Shuo Yang, Wei-Lin Chiang, Lianmin Zheng, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-rethinking-benchmark-contamination-language-models/)
655. **[LUT-GEMM: Quantized Matrix Multiplication based on LUTs for Efficient Inference in Large-Scale Generative Language Models](https://md.research.elamur.ai/free/en/papers/park-2022-lut-gemm-quantized-matrix-multiplication/) [International Conference on Learning Representations (ICLR)](https://md.research.elamur.ai/free/en/venues/international-conference-on-learning-representations-iclr/)**Gunho Park, Baeseong Park, Minsub Kim, et al.*2022 · 103 citations*
656. **[How do Large Language Models Handle Multilingualism?](https://md.research.elamur.ai/free/en/papers/zhao-2024-large-language-models-handle-multilingualism/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yiran Zhao, Wenxuan Zhang, Guizhen Chen, et al.*2024 · 102 citations*
657. **[BLOOM+1: Adding Language Support to BLOOM for Zero-Shot Prompting](https://md.research.elamur.ai/free/en/papers/yong-2023-bloom-1-adding-language-support/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zheng Xin Yong, Hailey Schoelkopf, Niklas Muennighoff, et al.*2023 · 102 citations*
658. [**PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization arXiv:2310.16427**Xinyuan Wang, Chenxi Li, Zhen Wang, et al.*2023 · 102 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-promptagent-strategic-planning-language-models/)
659. [**The Entropy Mechanism of Reinforcement Learning for Reasoning Language Models arXiv:2505.22617**Ganqu Cui, Yuchen Zhang, Jiacheng Chen, et al.*2025 · 101 citations*](https://md.research.elamur.ai/free/en/papers/cui-2025-entropy-mechanism-reinforcement-learning-reasoning/)
660. **[A Careful Examination of Large Language Model Performance on Grade School Arithmetic](https://md.research.elamur.ai/free/en/papers/zhang-2024-careful-examination-large-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hugh Zhang, Jeff Da, Dean Lee, et al.*2024 · 101 citations*
661. [**Debating with More Persuasive LLMs Leads to More Truthful Answers arXiv:2402.06782**Akbir Khan, John Hughes, Dan Valentine, et al.*2024 · 101 citations*](https://md.research.elamur.ai/free/en/papers/khan-2024-debating-more-persuasive-llms-leads/)
662. [**FireAct: Toward Language Agent Fine-tuning arXiv:2310.05915**Baian Chen, Chang Shu, Ehsan Shareghi, et al.*2023 · 101 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-fireact-toward-language-agent-fine/)
663. [**SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning arXiv:2308.00436**Ning Miao, Yee Whye Teh, Tom Rainforth*2023 · 101 citations*](https://md.research.elamur.ai/free/en/papers/miao-2023-selfcheck-using-llms-zero-shot/)
664. [**Rationale-Augmented Ensembles in Language Models arXiv:2207.00747**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 101 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-rationale-augmented-ensembles-language-models/)
665. [**FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets arXiv:2307.10928**Seonghyeon Ye, Doyoung Kim, Sungdong Kim, et al.*2023 · 100 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-flask-fine-grained-language-model/)
666. **[Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models](https://md.research.elamur.ai/free/en/papers/su-2024-unsupervised-real-time-hallucination-detection/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Weihang Su, Changyue Wang, Qingyao Ai, et al.*2024 · 99 citations*
667. [**World Model on Million-Length Video And Language With Blockwise RingAttention arXiv:2402.08268**Hao Liu, Wilson Yan, Matei Zaharia, et al.*2024 · 99 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-world-model-million-length-video/)
668. **[Answering Questions by Meta-Reasoning over Multiple Chains of Thought](https://md.research.elamur.ai/free/en/papers/yoran-2023-answering-questions-meta-reasoning-over/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ori Yoran, Tomer Wolfson, Ben Bogin, et al.*2023 · 99 citations*
669. **[FELM: Benchmarking Factuality Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2023-felm-benchmarking-factuality-evaluation-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shiqi Chen, Yiran Zhao, Jinghan Zhang, et al.*2023 · 99 citations*
670. [**MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning arXiv:2310.03731**Ke Wang, Houxing Ren, Aojun Zhou, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-mathcoder-seamless-code-integration-llms/)
671. [**Chain of Draft: Thinking Faster by Writing Less arXiv:2502.18600**Silei Xu, Wenhao Xie, Lingxiao Zhao, et al.*2025 · 98 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-chain-draft-thinking-faster-writing/)
672. [**Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing arXiv:2404.14618**Dujian Ding, Ankur Mallick, Chi Wang, et al.*2024 · 98 citations*](https://md.research.elamur.ai/free/en/papers/ding-2024-hybrid-llm-cost-efficient-quality/)
673. [**LongAlign: A Recipe for Long Context Alignment of Large Language Models arXiv:2401.18058**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2024 · 98 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longalign-recipe-long-context/)
674. [**LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding arXiv:2306.17107**Yanzhe Zhang, Ruiyi Zhang, Jiuxiang Gu, et al.*2023 · 98 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-llavar-enhanced-visual-instruction-tuning/)
675. [**Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints arXiv:2212.05055**Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, et al.*2022 · 98 citations*](https://md.research.elamur.ai/free/en/papers/komatsuzaki-2022-sparse-upcycling-training-mixture-experts/)
676. [**Safety Alignment Should Be Made More Than Just a Few Tokens Deep arXiv:2406.05946**Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, et al.*2024 · 97 citations*](https://md.research.elamur.ai/free/en/papers/qi-2024-safety-alignment-should-made-more/)
677. [**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/)
678. **[Inseq: An Interpretability Toolkit for Sequence Generation Models](https://md.research.elamur.ai/free/en/papers/sarti-2023-inseq-interpretability-toolkit-sequence-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabriele Sarti, Nils Feldhus, Ludwig Sickert, et al.*2023 · 97 citations*
679. **[MAmmoTH2: Scaling Instructions from the Web](https://md.research.elamur.ai/free/en/papers/yue-2024-mammoth2-scaling-instructions-web/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xiang Yue, Tuney Zheng, Ge Zhang, et al.*2024 · 96 citations*
680. [**TextGrad: Automatic "Differentiation" via Text arXiv:2406.07496**Mert Yuksekgonul, Federico Bianchi, Joseph Boen, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/yuksekgonul-2024-textgrad-automatic-differentiation-text/)
681. [**The Platonic Representation Hypothesis arXiv:2405.07987**Minyoung Huh, Brian Cheung, Tongzhou Wang, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/huh-2024-platonic-representation-hypothesis/)
682. [**Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation arXiv:2305.15011**Haonan Li, Fajri Koto, Minghao Wu, et al.*2023 · 96 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-bactrian-x-multilingual-replicable-instruction/)
683. [**INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models arXiv:2306.04757**Yew Ken Chia, Pengfei Hong, Lidong Bing, et al.*2023 · 96 citations*](https://md.research.elamur.ai/free/en/papers/chia-2023-instructeval-towards-holistic-evaluation-instruction/)
684. [**Teaching Algorithmic Reasoning via In-context Learning arXiv:2211.09066**Hattie Zhou, Azade Nova, Hugo Larochelle, et al.*2022 · 96 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-teaching-algorithmic-reasoning-context-learning/)
685. [**Multi-Agent Collaboration Mechanisms: A Survey of LLMs arXiv:2501.06322**Khanh-Tung Tran, Dung Dao, Minh-Duong Nguyen, et al.*2025 · 95 citations*](https://md.research.elamur.ai/free/en/papers/tran-2025-multi-agent-collaboration-mechanisms/)
686. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
687. **[InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback](https://md.research.elamur.ai/free/en/papers/yang-2023-intercode-standardizing-benchmarking-interactive-coding/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**John Yang, Akshara Prabhakar, Karthik Narasimhan, et al.*2023 · 95 citations*
688. **[NLPositionality: Characterizing Design Biases of Datasets and Models](https://md.research.elamur.ai/free/en/papers/santy-2023-nlpositionality-characterizing-design-biases-datasets/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sebastin Santy, Jenny Liang, Ronan Le Bras, et al.*2023 · 95 citations*
689. **[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*
690. **[Truncation Sampling as Language Model Desmoothing](https://md.research.elamur.ai/free/en/papers/hewitt-2022-truncation-sampling-as-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**John Hewitt, Christopher Manning, Percy Liang*2022 · 95 citations*
691. **[Boosting Language Models Reasoning with Chain-of-Knowledge Prompting](https://md.research.elamur.ai/free/en/papers/wang-2024-boosting-language-models-reasoning-chain/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianing Wang, Qiushi Sun, Xiang Li, et al.*2024 · 94 citations*
692. [**InternLM-XComposer2: Mastering Free-form Text-Image Composition and Comprehension in Vision-Language Large Model arXiv:2401.16420**Xiaoyi Dong, Pan Zhang, Yuhang Zang, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-internlm-xcomposer2-mastering-free/)
693. **[Moral Foundations of Large Language Models](https://md.research.elamur.ai/free/en/papers/abdulhai-2024-moral-foundations-large-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Marwa Abdulhai, Gregory Serapio-García, Clement Crepy, et al.*2024 · 94 citations*
694. **[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*
695. [**Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions arXiv:2306.02224**Hui Yang, Sifu Yue, Yunzhong He*2023 · 94 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-auto-gpt-online-decision-making/)
696. **[Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate](https://md.research.elamur.ai/free/en/papers/xiong-2023-examining-inter-consistency-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kai Xiong, Xiao Ding, Yixin Cao, et al.*2023 · 94 citations*
697. **[LLMs Are Few-Shot In-Context Low-Resource Language Learners](https://md.research.elamur.ai/free/en/papers/cahyawijaya-2024-llms-are-few-shot-context/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Samuel Cahyawijaya, Holy Lovenia, Pascale Fung*2024 · 93 citations*
698. **[LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin](https://md.research.elamur.ai/free/en/papers/dou-2024-loramoe-alleviating-world-knowledge-forgetting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shihan Dou, Enyu Zhou, Yan Liu, et al.*2024 · 93 citations*
699. [**How well do Large Language Models perform in Arithmetic tasks? arXiv:2304.02015**Zheng Yuan, Hongyi Yuan, Chuanqi Tan, et al.*2023 · 93 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-well-large-language-models-perform/)
700. **[WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2024-wildteaming-scale-wild-jailbreaks-adversarially/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Liwei Jiang, Kavel Rao, Seungju Han, et al.*2024 · 92 citations*

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