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# 🧠 Language models & pretraining

1,343 papers · page 10 of 15 · 826–925 · the most cited first · [all topics](https://md.research.elamur.ai/free/en/topics/)

Models that learn a language from raw text, and how they are pretrained, tuned and prompted.

## Papers (1,343)

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826. **[LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models](https://md.research.elamur.ai/free/en/papers/parmar-2024-logicbench-systematic-evaluation-logical-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mihir Parmar, Nisarg Patel, Neeraj Varshney, et al.*2024 · 92 citations*
827. [**INSTRUCTEVAL: Towards Holistic Evaluation of Instruction-Tuned Large Language Models arXiv:2306.04757**Yew Ken Chia, Pengfei Hong, Lidong Bing, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/chia-2023-instructeval-towards-holistic-evaluation-instruction/)
828. [**SeaLLMs — Large Language Models for Southeast Asia arXiv:2312.00738**Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2023-seallms-large-language-models-southeast/)
829. **[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 · 92 citations*
830. **[Do-Not-Answer: Evaluating Safeguards in LLMs](https://md.research.elamur.ai/free/en/papers/wang-2024-do-not-answer-evaluating-safeguards/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuxia Wang, Haonan Li, Xudong Han, et al.*2024 · 91 citations*
831. [**Large Language Models are Inconsistent and Biased Evaluators arXiv:2405.01724**Rickard Stureborg, Dimitris Alikaniotis, Yoshi Suhara*2024 · 91 citations*](https://md.research.elamur.ai/free/en/papers/stureborg-2024-large-language-models-inconsistent-biased/)
832. **[PyramidInfer: Pyramid KV Cache Compression for High-throughput LLM Inference](https://md.research.elamur.ai/free/en/papers/yang-2024-pyramidinfer-pyramid-kv-cache-compression/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dongjie Yang, Xiaodong Han, Yan Gao, et al.*2024 · 91 citations*
833. [**ChatHaruhi: Reviving Anime Character in Reality via Large Language Model arXiv:2308.09597**Cheng Li, Ziang Leng, Chenxi Yan, et al.*2023 · 91 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-chatharuhi-reviving-anime-character-reality/)
834. [**Skywork: A More Open Bilingual Foundation Model arXiv:2310.19341**Tianwen Wei, Liang Zhao, Lichang Zhang, et al.*2023 · 91 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-skywork-more-open-bilingual/)
835. **[As Good as New. How to Successfully Recycle English GPT-2 to Make Models for Other Languages](https://md.research.elamur.ai/free/en/papers/vries-2021-as-good-as-new-how/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wietse de Vries, Malvina Nissim*2021 · 91 citations*
836. **[Aligning Language Models to User Opinions](https://md.research.elamur.ai/free/en/papers/hwang-2023-aligning-language-models-user-opinions/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**EunJeong Hwang, Bodhisattwa Majumder, Niket Tandon*2023 · 90 citations*
837. **[Cross-Lingual Consistency of Factual Knowledge in Multilingual Language Models](https://md.research.elamur.ai/free/en/papers/qi-2023-cross-lingual-consistency-factual-knowledge/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jirui Qi, Raquel Fernández, Arianna Bisazza*2023 · 90 citations*
838. **[PLACES: Prompting Language Models for Social Conversation Synthesis](https://md.research.elamur.ai/free/en/papers/chen-2023-places-prompting-language-models-social/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Maximillian Chen, Alexandros Papangelis, Chenyang Tao, et al.*2023 · 90 citations*
839. **[WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models](https://md.research.elamur.ai/free/en/papers/minixhofer-2022-wechsel-effective-initialization-subword-embeddings/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Benjamin Minixhofer, Fabian Paischer, Navid Rekabsaz*2022 · 90 citations*
840. **[Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs](https://md.research.elamur.ai/free/en/papers/jin-2024-graph-chain-thought-augmenting-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bowen Jin, Chulin Xie, Jiawei Zhang, et al.*2024 · 89 citations*
841. **[Optimizing Instructions and Demonstrations for Multi-Stage Language Model Programs](https://md.research.elamur.ai/free/en/papers/ong-2024-optimizing-instructions-demonstrations-multi-stage/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Krista Opsahl-Ong, Michael J Ryan, Josh Purtell, et al.*2024 · 89 citations*
842. **[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 · 89 citations*
843. **[“My Answer is C”: First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-my-answer-is-c-first/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinpeng Wang, Bolei Ma, Chengzhi Hu, et al.*2024 · 89 citations*
844. **[Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks](https://md.research.elamur.ai/free/en/papers/sottana-2023-evaluation-metrics-era-gpt-4/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Andrea Sottana, Bin Liang, Kai Zou, et al.*2023 · 89 citations*
845. [**Language Modeling Is Compression arXiv:2309.10668**Gregoire Deletang, Anian Ruoss, Paul-Ambroise Duquenne, et al.*2023 · 89 citations*](https://md.research.elamur.ai/free/en/papers/deletang-2023-language-modeling-compression/)
846. **[Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?](https://md.research.elamur.ai/free/en/papers/merrill-2021-provable-limitations-acquiring-meaning-ungrounded/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**William Merrill, Yoav Goldberg, Roy Schwartz, et al.*2021 · 89 citations*
847. **[Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models](https://md.research.elamur.ai/free/en/papers/lu-2024-not-all-experts-are-equal/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xudong Lu, Qi Liu, Yuhui Xu, et al.*2024 · 88 citations*
848. [**Small Language Models: Survey, Measurements, and Insights arXiv:2409.15790**Zhenyan Lu, Xiang Li, Dongqi Cai, et al.*2024 · 88 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-small-language-models-survey-measurements/)
849. [**On the Tool Manipulation Capability of Open-source Large Language Models arXiv:2305.16504**Qiantong Xu, Fenglu Hong, Bo Li, et al.*2023 · 88 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-tool-manipulation-capability-open-source/)
850. [**A Survey of Large Language Models in Medicine: Progress, Application, and Challenge arXiv:2311.05112**Hongjian Zhou, Fenglin Liu, Boyang Gu, et al.*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-survey-large-language-models-medicine/)
851. **[Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment](https://md.research.elamur.ai/free/en/papers/lu-2024-large-language-models-are-superpositions/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Keming Lu, Bowen Yu, Chang Zhou, et al.*2024 · 86 citations*
852. **[LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models](https://md.research.elamur.ai/free/en/papers/liusie-2024-llm-comparative-assessment-zero-shot/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Adian Liusie, Potsawee Manakul, Mark Gales*2024 · 86 citations*
853. **[Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/shi-2024-math-llava-bootstrapping-mathematical-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Wenhao Shi, Zhiqiang Hu, Yi Bin, et al.*2024 · 86 citations*
854. **[Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement](https://md.research.elamur.ai/free/en/papers/xu-2024-pride-prejudice-llm-amplifies-self/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenda Xu, Guanglei Zhu, Xuandong Zhao, et al.*2024 · 86 citations*
855. **[The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts](https://md.research.elamur.ai/free/en/papers/shen-2024-language-barrier-dissecting-safety-challenges/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lingfeng Shen, Weiting Tan, Sihao Chen, et al.*2024 · 86 citations*
856. [**PromptNER: Prompting For Named Entity Recognition arXiv:2305.15444**Dhananjay Ashok, Zachary C. Lipton*2023 · 86 citations*](https://md.research.elamur.ai/free/en/papers/ashok-2023-promptner-prompting-named-entity-recognition/)
857. [**Universal Self-Consistency for Large Language Model Generation arXiv:2311.17311**Xinyun Chen, Renat Aksitov, Uri Alon, et al.*2023 · 86 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-universal-self-consistency-large-language/)
858. **[AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models](https://md.research.elamur.ai/free/en/papers/madabushi-2021-astitchinlanguagemodels-dataset-methods-exploration-idiomaticity/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Harish Tayyar Madabushi, Edward Gow-Smith, Carolina Scarton, et al.*2021 · 86 citations*
859. **[How to Adapt Your Pretrained Multilingual Model to 1600 Languages](https://md.research.elamur.ai/free/en/papers/ebrahimi-2021-how-adapt-your-pretrained-multilingual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Abteen Ebrahimi, Katharina Kann*2021 · 86 citations*
860. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
861. **[ADaPT: As-Needed Decomposition and Planning with Language Models](https://md.research.elamur.ai/free/en/papers/prasad-2024-adapt-as-needed-decomposition-planning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Archiki Prasad, Alexander Koller, Mareike Hartmann, et al.*2024 · 85 citations*
862. **[FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models](https://md.research.elamur.ai/free/en/papers/jiang-2024-followbench-multi-level-fine-grained/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxin Jiang, Yufei Wang, Xingshan Zeng, et al.*2024 · 85 citations*
863. **[Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment](https://md.research.elamur.ai/free/en/papers/raina-2024-is-llm-as-judge-robust/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Vyas Raina, Adian Liusie, Mark Gales*2024 · 85 citations*
864. **[LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-Training](https://md.research.elamur.ai/free/en/papers/zhu-2024-llama-moe-building-mixture-experts/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tong Zhu, Xiaoye Qu, Daize Dong, et al.*2024 · 85 citations*
865. **[LLMs cannot find reasoning errors, but can correct them given the error location](https://md.research.elamur.ai/free/en/papers/tyen-2024-llms-cannot-find-reasoning-errors/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Gladys Tyen, Hassan Mansoor, Victor Carbune, et al.*2024 · 85 citations*
866. **[SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2024-seaeval-multilingual-foundation-models-cross/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bin Wang, Zhengyuan Liu, Xin Huang, et al.*2024 · 85 citations*
867. **[When Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages](https://md.research.elamur.ai/free/en/papers/chang-2024-when-is-multilinguality-curse-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tyler A. Chang, Catherine Arnett, Zhuowen Tu, et al.*2024 · 85 citations*
868. **[Multilingual Large Language Models Are Not (Yet) Code-Switchers](https://md.research.elamur.ai/free/en/papers/zhang-2023-multilingual-large-language-models-are/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ruochen Zhang, Samuel Cahyawijaya, Jan Christian Blaise Cruz, et al.*2023 · 85 citations*
869. **[Solving Math Word Problems via Cooperative Reasoning induced Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2023-solving-math-word-problems-cooperative/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyu Zhu, Junjie Wang, Lin Zhang, et al.*2023 · 85 citations*
870. **[Finding Skill Neurons in Pre-trained Transformer-based Language Models](https://md.research.elamur.ai/free/en/papers/wang-2022-finding-skill-neurons-pre-trained/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaozhi Wang, Kaiyue Wen, Zhengyan Zhang, et al.*2022 · 85 citations*
871. **[How to Train Long-Context Language Models (Effectively)](https://md.research.elamur.ai/free/en/papers/gao-2025-how-train-long-context-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Gao, Alexander Wettig, Howard Yen, et al.*2025 · 84 citations*
872. **[Detoxifying Large Language Models via Knowledge Editing](https://md.research.elamur.ai/free/en/papers/wang-2024-detoxifying-large-language-models-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengru Wang, Ningyu Zhang, Ziwen Xu, et al.*2024 · 84 citations*
873. **[Machine Unlearning of Pre-trained Large Language Models](https://md.research.elamur.ai/free/en/papers/yao-2024-machine-unlearning-pre-trained-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jin Yao, Eli Chien, Minxin Du, et al.*2024 · 84 citations*
874. **[Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning](https://md.research.elamur.ai/free/en/papers/tan-2024-democratizing-large-language-models-personalized/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, et al.*2024 · 83 citations*
875. **[MMC: Advancing Multimodal Chart Understanding with Large-scale Instruction Tuning](https://md.research.elamur.ai/free/en/papers/liu-2024-mmc-advancing-multimodal-chart-understanding/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Fuxiao Liu, Xiaoyang Wang, Wenlin Yao, et al.*2024 · 83 citations*
876. [**Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks arXiv:2310.10844**Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, et al.*2023 · 83 citations*](https://md.research.elamur.ai/free/en/papers/shayegani-2023-survey-vulnerabilities-large-language-models/)
877. **[EfficientQAT: Efficient Quantization-Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-efficientqat-efficient-quantization-aware-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mengzhao Chen, Wenqi Shao, Peng Xu, et al.*2025 · 82 citations*
878. **[Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples](https://md.research.elamur.ai/free/en/papers/saparov-2023-testing-general-deductive-reasoning-capacity/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, et al.*2023 · 82 citations*
879. **[Tree of Clarifications: Answering Ambiguous Questions with Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/kim-2023-tree-clarifications-answering-ambiguous-questions/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Gangwoo Kim, Sungdong Kim, Byeongguk Jeon, et al.*2023 · 82 citations*
880. **[The Geometry of Multilingual Language Model Representations](https://md.research.elamur.ai/free/en/papers/chang-2022-geometry-multilingual-language-model-representations/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tyler A. Chang, Zhuowen Tu, Benjamin K. Bergen*2022 · 82 citations*
881. **[Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction](https://md.research.elamur.ai/free/en/papers/gerz-2018-language-modeling-morphologically-rich-languages/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Daniela Gerz, Ivan Vulić, Edoardo Ponti, et al.*2018 · 82 citations*
882. **[OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models](https://md.research.elamur.ai/free/en/papers/huang-2025-opencoder-open-cookbook-top-tier/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Siming Huang, Tianhao Cheng, Jason Klein Liu, et al.*2025 · 81 citations*
883. **[PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference](https://md.research.elamur.ai/free/en/papers/ji-2025-pku-saferlhf-multi-level-safety/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiaming Ji, Donghai Hong, Borong Zhang, et al.*2025 · 81 citations*
884. **[Multilingual LLMs are Better Cross-lingual In-context Learners with Alignment](https://md.research.elamur.ai/free/en/papers/tanwar-2023-multilingual-llms-are-better-cross/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eshaan Tanwar, Subhabrata Dutta, Manish Borthakur, et al.*2023 · 81 citations*
885. [**ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs arXiv:2402.11753**Fengqing Jiang, Zhangchen Xu, Luyao Niu, et al.*2024 · 80 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-artprompt-ascii-art-based-jailbreak/)
886. [**Do Membership Inference Attacks Work on Large Language Models? arXiv:2402.07841**Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.*2024 · 80 citations*](https://md.research.elamur.ai/free/en/papers/duan-2024-membership-inference-attacks-work-large/)
887. **[A Language Modeling Approach to Predicting Reading Difficulty](https://md.research.elamur.ai/free/en/papers/thompson-2004-language-modeling-approach-predicting-reading/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kevyn Collins-Thompson, James P. Callan*2004 · 80 citations*
888. **[Generating Benchmarks for Factuality Evaluation of Language Models](https://md.research.elamur.ai/free/en/papers/muhlgay-2024-generating-benchmarks-factuality-evaluation-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Dor Muhlgay, Ori Ram, Inbal Magar, et al.*2024 · 79 citations*
889. **[MART: Improving LLM Safety with Multi-round Automatic Red-Teaming](https://md.research.elamur.ai/free/en/papers/ge-2024-mart-improving-llm-safety-multi/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Suyu Ge, Chunting Zhou, Rui Hou, et al.*2024 · 79 citations*
890. **[D4: Improving LLM Pretraining via Document De-Duplication and Diversification](https://md.research.elamur.ai/free/en/papers/tirumala-2023-d4-improving-llm-pretraining-document/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kushal Tirumala, Daniel Simig, Armen Aghajanyan, et al.*2023 · 79 citations*
891. **[Mitigating Label Biases for In-context Learning](https://md.research.elamur.ai/free/en/papers/fei-2023-mitigating-label-biases-context-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yu Fei, Yifan Hou, Zeming Chen, et al.*2023 · 79 citations*
892. **[Are Pretrained Language Models Symbolic Reasoners over Knowledge?](https://md.research.elamur.ai/free/en/papers/kassner-2020-are-pretrained-language-models-symbolic/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Nora Kassner, Benno Krojer, Hinrich Schütze*2020 · 79 citations*
893. [**LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning arXiv:2401.01325**Hongye Jin, Xiaotian Han, Jingfeng Yang, et al.*2024 · 78 citations*](https://md.research.elamur.ai/free/en/papers/jin-2024-llm-maybe-longlm-self-extend/)
894. **[Multilingual Instruction Tuning With Just a Pinch of Multilinguality](https://md.research.elamur.ai/free/en/papers/shaham-2024-multilingual-instruction-tuning-just-pinch/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Uri Shaham, Jonathan Herzig, Roee Aharoni, et al.*2024 · 78 citations*
895. **[Finding Support Examples for In-Context Learning](https://md.research.elamur.ai/free/en/papers/li-2023-finding-support-examples-context-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaonan Li, Xipeng Qiu*2023 · 78 citations*
896. [**LLMs Get Lost In Multi-Turn Conversation arXiv:2505.06120**Philippe Laban, Hiroaki Hayashi, Yingbo Zhou, et al.*2025 · 77 citations*](https://md.research.elamur.ai/free/en/papers/laban-2025-llms-get-lost-multi-turn/)
897. **[Style Over Substance: Evaluation Biases for Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2025-style-over-substance-evaluation-biases/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Minghao Wu, Alham Fikri Aji*2025 · 77 citations*
898. [**Humans or LLMs as the Judge? A Study on Judgement Biases arXiv:2402.10669**Guiming Hardy Chen, Shunian Chen, Ziche Liu, et al.*2024 · 77 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-humans-llms-judge-study-judgement/)
899. **[Lookback Lens: Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps](https://md.research.elamur.ai/free/en/papers/chuang-2024-lookback-lens-detecting-mitigating-contextual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yung-Sung Chuang, Linlu Qiu, Cheng-Yu Hsieh, et al.*2024 · 76 citations*
900. [**Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs arXiv:2308.13387**Yuxia Wang, Haonan Li, Xudong Han, et al.*2023 · 76 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-answer-dataset-evaluating-safeguards-llms/)
901. **[AudioBench: A Universal Benchmark for Audio Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-audiobench-universal-benchmark-audio-large/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bin Wang, Xunlong Zou, Geyu Lin, et al.*2025 · 75 citations*
902. **[In-Context Learning with Long-Context Models: An In-Depth Exploration](https://md.research.elamur.ai/free/en/papers/bertsch-2025-context-learning-long-context-models/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Amanda Bertsch, Maor Ivgi, Emily Xiao, et al.*2025 · 75 citations*
903. **[MoBA: Mixture of Block Attention for Long-Context LLMs](https://md.research.elamur.ai/free/en/papers/lu-2025-moba-mixture-block-attention/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Enzhe Lu, Zhejun Jiang, Jingyuan Liu, et al.*2025 · 75 citations*
904. [**Aligning with Human Judgement: The Role of Pairwise Preference in Large Language Model Evaluators arXiv:2403.16950**Yinhong Liu, Han Zhou, Zhijiang Guo, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-aligning-human-judgement-role-pairwise/)
905. [**PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities**Settaluri Sravanthi, Meet Doshi, Pavan Tankala, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/sravanthi-2024-pub-pragmatics-understanding-benchmark-assessing/)
906. **[Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting](https://md.research.elamur.ai/free/en/papers/chen-2023-dipping-plms-sauce-bridging-structure/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chen Chen, Yufei Wang, Aixin Sun, et al.*2023 · 75 citations*
907. [**SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling arXiv:2312.15166**Dahyun Kim, Chanjun Park, Sanghoon Kim, et al.*2023 · 75 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-solar-10-7b-scaling-large/)
908. **[MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation](https://md.research.elamur.ai/free/en/papers/xuan-2025-mmlu-prox-multilingual-benchmark-advanced/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Weihao Xuan, Rui Yang, Heli Qi, et al.*2025 · 74 citations*
909. [**Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration arXiv:2306.09093**Chenyang Lyu, Minghao Wu, Longyue Wang, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/lyu-2023-macaw-llm-multi-modal-language/)
910. **[Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker](https://md.research.elamur.ai/free/en/papers/sclar-2023-minding-language-models-lack-theory/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Melanie Sclar, Sachin Kumar, Peter West, et al.*2023 · 74 citations*
911. [**Wider and Deeper LLM Networks are Fairer LLM Evaluators arXiv:2308.01862**Xinghua Zhang, Bowen Yu, Haiyang Yu, et al.*2023 · 74 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-wider-deeper-llm-networks-fairer/)
912. **[Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions](https://md.research.elamur.ai/free/en/papers/masoud-2025-cultural-alignment-large-language-models/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Reem I. Masoud, Ziquan Liu, Martin Ferianc, et al.*2025 · 73 citations*
913. **[Is LLM a Reliable Reviewer? A Comprehensive Evaluation of LLM on Automatic Paper Reviewing Tasks](https://md.research.elamur.ai/free/en/papers/zhou-2024-is-llm-reliable-reviewer-comprehensive/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ruiyang Zhou, Lu Chen, Kai Yu*2024 · 73 citations*
914. [**OR-Bench: An Over-Refusal Benchmark for Large Language Models arXiv:2405.20947**Justin Cui, Wei-Lin Chiang, Ion Stoica, et al.*2024 · 73 citations*](https://md.research.elamur.ai/free/en/papers/cui-2024-bench-over-refusal-benchmark-large/)
915. **[CLiMP: A Benchmark for Chinese Language Model Evaluation](https://md.research.elamur.ai/free/en/papers/xiang-2021-climp-benchmark-chinese-language-model/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Beilei Xiang, Changbing Yang, Yu Li, et al.*2021 · 73 citations*
916. [**Gecko: Versatile Text Embeddings Distilled from Large Language Models arXiv:2403.20327**Jinhyuk Lee, Zhuyun Dai, Xiaoqi Ren, et al.*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-gecko-versatile-text-embeddings-distilled/)
917. [**Self-Preference Bias in LLM-as-a-Judge arXiv:2410.21819**Koki Wataoka, Tsubasa Takahashi, Ryokan Ri*2024 · 72 citations*](https://md.research.elamur.ai/free/en/papers/wataoka-2024-self-preference-bias-llm-judge/)
918. **[CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models](https://md.research.elamur.ai/free/en/papers/qian-2023-creator-tool-creation-disentangling-abstract/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cheng Qian, Chi Han, Yi Fung, et al.*2023 · 72 citations*
919. **[LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing](https://md.research.elamur.ai/free/en/papers/du-2024-llms-assist-nlp-researchers-critique/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiangshu Du, Yibo Wang, Wenting Zhao, et al.*2024 · 71 citations*
920. **[Towards Safer Large Language Models through Machine Unlearning](https://md.research.elamur.ai/free/en/papers/liu-2024-safer-large-language-models-through/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, et al.*2024 · 71 citations*
921. **[Unveiling the Implicit Toxicity in Large Language Models](https://md.research.elamur.ai/free/en/papers/wen-2023-unveiling-implicit-toxicity-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiaxin Wen, Pei Ke, Hao Sun, et al.*2023 · 71 citations*
922. **[Aligning Large and Small Language Models via Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/ranaldi-2024-aligning-large-small-language-models/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Leonardo Ranaldi, Andre Freitas*2024 · 70 citations*
923. **[IrokoBench: A New Benchmark for African Languages in the Age of Large Language Models](https://md.research.elamur.ai/free/en/papers/adelani-2025-irokobench-new-benchmark-african-languages/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**David Ifeoluwa Adelani, Jessica Ojo, Israel Abebe Azime, et al.*2025 · 69 citations*
924. **[Mission: Impossible Language Models](https://md.research.elamur.ai/free/en/papers/kallini-2024-mission-impossible-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Julie Kallini, Isabel Papadimitriou, Richard Futrell, et al.*2024 · 69 citations*
925. [**Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? arXiv:2309.07462**Rishav Hada, Varun Gumma, Adrian de Wynter, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/hada-2023-large-language-model-based-evaluators/)

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