🔗 Who cites «Evaluating the Moral Beliefs Encoded in LLMs»

100 papers of our library cite it (of the 149,439 whose bibliographies we read) · 32 with a page here, the most cited first · ← back to «Evaluating the Moral Beliefs Encoded in LLMs»

1. TrustLLM: Trustworthiness in Large Language ModelsarXiv:2401.05561Yue Huang, Lichao Sun, Haoran Wang, et al.2024 · 237 citations
2. Evaluating Large Language Models: A Comprehensive SurveyarXiv:2310.19736Zishan Guo, Renren Jin, Chuang Liu, et al.2023 · 182 citations
3. AI Alignment: A Comprehensive SurveyarXiv:2310.19852Jiaming Ji, Tianyi Qiu, Boyuan Chen, et al.2023 · 173 citations
4. A Roadmap to Pluralistic AlignmentarXiv:2402.05070Taylor Sorensen, Jared Moore, Jillian Fisher, et al.2024 · 143 citations
5. DeepInception: Hypnotize Large Language Model to Be JailbreakerarXiv:2311.03191Xuan Li, Zhanke Zhou, Jianing Zhu, et al.2023 · 141 citations
6. Do LLMs Exhibit Human-like Response Biases? A Case Study in Survey DesignTransactions of the Association for Computational Linguistics (TACL)Lindia Tjuatja, Valerie Chen, Tongshuang Wu, et al.2024 · 140 citations
7. Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Paul Röttger, Valentin Hofmann, Valentina Pyatkin, et al.2024 · 137 citations
8. LLM Self Defense: By Self Examination, LLMs Know They Are Being TrickedarXiv:2308.07308Mansi Phute, Alec Helbling, Matthew Hull, et al.2023 · 119 citations
9. Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and DutiesProceedings of the AAAI Conference on Artificial IntelligenceTaylor Sorensen, Liwei Jiang, Jena Hwang, et al.2023 · 116 citations
10. From Persona to Personalization: A Survey on Role-Playing Language AgentsarXiv:2404.18231Jiangjie Chen, Xintao Wang, Rui Xu, et al.2024 · 115 citations
11. Moral Foundations of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Marwa Abdulhai, Gregory Serapio-García, Clement Crepy, et al.2024 · 94 citations
12. “My Answer is C”: First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language ModelsFindings of the Association for Computational LinguisticsXinpeng Wang, Bolei Ma, Chengzhi Hu, et al.2024 · 91 citations
13. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
14. Modular Pluralism: Pluralistic Alignment via Multi-LLM CollaborationConference on Empirical Methods in Natural Language Processing (EMNLP)Shangbin Feng, Taylor Sorensen, Yuhan Liu, et al.2024 · 68 citations
15. ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yuanyi Ren, Haoran Ye, Hanjun Fang, et al.2024 · 43 citations
16. MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment TasksConference on Neural Information Processing Systems (NeurIPS)Allen Nie, Yuhui Zhang, Atharva Amdekar, et al.2023 · 41 citations
17. Introducing v0.5 of the AI Safety Benchmark from MLCommonsarXiv:2404.12241Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, et al.2024 · 38 citations
18. SafetyPrompts: a Systematic Review of Open Datasets for Evaluating and Improving Large Language Model SafetyarXiv:2404.05399Paul Rottger, Fabio Pernisi, Bertie Vidgen, et al.2024 · 33 citations
19. Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the AboveAnnual Meeting of the Association for Computational Linguistics (ACL)Nishant Balepur, Rachel Rudinger, Jordan Boyd-Graber2025 · 28 citations
20. Language Model Alignment in Multilingual Trolley ProblemsarXiv:2407.02273Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, et al.2024 · 23 citations
21. Multiple LLM Agents Debate for Equitable Cultural AlignmentAnnual Meeting of the Association for Computational Linguistics (ACL)Dayeon Ki, Rachel Rudinger, Tianyi Zhou, et al.2025 · 10 citations
22. Comparing Moral Values in Western English-speaking societies and LLMs with Word AssociationsAnnual Meeting of the Association for Computational Linguistics (ACL)Chaoyi Xiang, Chunhua Liu, Simon De Deyne, et al.2025 · 3 citations
23. Abstain-R1: Calibrated Abstention and Post-Refusal Clarification via Verifiable RLFindings of the Association for Computational LinguisticsHaotian Zhai, Jingcheng Liang, Dongyeop Kang2026 · 2 citations
24. The Pluralistic Moral Gap: Understanding Moral Judgment and Value Differences between Humans and Large Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Giuseppe Russo, Debora Nozza, Paul Röttger, et al.2026 · 1 citation
25. CoopValue: Revealing LLM Value Preferences Through Multi-Agent CooperationFindings of the Association for Computational LinguisticsZee Hen Tang, Mi-Yen Yeh2026
26. Do Emotions Influence Moral Judgment in Large Language Models?Findings of the Association for Computational LinguisticsMohammad Saim, Tianyu Jiang2026
27. Do Language Models Think Consistently? A Study of Value Preferences Across Varying Response LengthsFindings of the Association for Computational LinguisticsInderjeet Jayakumar Nair, Lu Wang2026
28. Don't Want Your LLM to Recommend Nuclear Strike? Try Asking It in JapaneseWorkshop on Trustworthy NLPRian Touchent2026
29. EMPATH: An Ensemble Method for Automatic Fine-Grained Turn-Level Dialogue Empathy Evaluation with a Novel Emotional Distance MetricFindings of the Association for Computational LinguisticsDongning Rao, Zhihua Liang, Zhihua Jiang2026
30. Evaluating Language Model Pluralism through In-the-wild Crowd DiscussionsAnnual Meeting of the Association for Computational Linguistics (ACL)Gagan Mundada, Rohan Surana, Nandhini Swaminathan, et al.2026
31. Probing the Plasticity and Correlation of LLM Value Systems: LLM Value Rankings are Not StableFindings of the Association for Computational LinguisticsZhenheng Tang, Qihua Pan, Jingya Shen, et al.2026
32. “Sorry, Can't Help You”: How Large Language Models Judge Failures to Help Across LanguagesWorkshop on Cross-Cultural Considerations in NLPPavithra P M Nair, Gilad Gressel, Krishnashree Achuthan2026

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