---
title: "Who cites Simple synthetic data reduces — 107 citing papers"
description: "107 papers of the Elamur Research library cite «Simple synthetic data reduces sycophancy in large language models», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/wei-2023-simple-synthetic-data-reduces-sycophancy/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wei-2023-simple-synthetic-data-reduces-sycophancy/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Simple synthetic data reduces sycophancy in large…](https://md.research.elamur.ai/free/en/papers/wei-2023-simple-synthetic-data-reduces-sycophancy/) / cited by

# Who cites «Simple synthetic data reduces sycophancy in large language models»

107 papers of our library cite it (of the 149,439 whose bibliographies we read) · 40 with a page here, the most cited first · [← back to «Simple synthetic data reduces sycophancy in large language models»](https://md.research.elamur.ai/free/en/papers/wei-2023-simple-synthetic-data-reduces-sycophancy/)

1. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
2. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
3. **[Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2023-siren-s-song-ai-ocean/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Yue Zhang, Yafu Li, Leyang Cui, et al.*2023 · 977 citations*
4. **[Explainability for Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 790 citations*
5. [**Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models arXiv:2401.01301 · Journal of Legal Analysis 16, no. 1 (2024): 64-93**Matthew Dahl, Varun Magesh, Mirac Suzgun, et al.*2024 · 437 citations*](https://md.research.elamur.ai/free/en/papers/dahl-2024-large-legal-fictions-profiling-legal/)
6. [**Towards Understanding Sycophancy in Language Models arXiv:2310.13548**Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.*2023 · 384 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2023-towards-understanding-sycophancy-language-models/)
7. [**MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning arXiv:2309.05653**Xiang Yue, Xingwei Qu, Ge Zhang, et al.*2023 · 295 citations*](https://md.research.elamur.ai/free/en/papers/yue-2023-mammoth-building-math-generalist-models/)
8. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
9. [**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/)
10. **[ELEPHANT: Measuring and understanding social sycophancy in LLMs](https://md.research.elamur.ai/free/en/papers/cheng-2025-elephant-measuring-understanding-social-sycophancy/) [Science](https://md.research.elamur.ai/free/en/venues/science/)**Myra Cheng, Sunny Yu, Cinoo Lee, et al.*2025 · 188 citations*
11. **[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*
12. **[Sycophancy in Large Language Models: Causes and Mitigations](https://md.research.elamur.ai/free/en/papers/malmqvist-2024-sycophancy-large-language-models-causes/) [Lecture notes in networks and systems](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-networks-and-systems/)**Lars Malmqvist*2024 · 123 citations*
13. [**Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models arXiv:2501.09686**Fengli Xu, Qianyue Hao, Zefang Zong, et al.*2025 · 120 citations*](https://md.research.elamur.ai/free/en/papers/xu-2025-towards-large-reasoning-models-survey/)
14. [**Best Practices and Lessons Learned on Synthetic Data arXiv:2404.07503**Ruibo Liu, Jerry Wei, Fangyu Liu, et al.*2024 · 119 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-best-practices-lessons-learned-synthetic/)
15. **[GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers](https://md.research.elamur.ai/free/en/papers/li-2024-gsm-plus-comprehensive-benchmark-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qintong Li, Leyang Cui, Xueliang Zhao, et al.*2024 · 108 citations*
16. **[R-Tuning: Instructing Large Language Models to Say ‘I Don't Know'](https://md.research.elamur.ai/free/en/papers/zhang-2024-r-tuning-instructing-large-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hanning Zhang, Shizhe Diao, Yong Lin, et al.*2024 · 99 citations*
17. **[Alignment for Honesty](https://md.research.elamur.ai/free/en/papers/yang-2023-alignment-honesty/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuqing Yang, Ethan Chern, Xipeng Qiu, et al.*2023 · 90 citations*
18. [**MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use arXiv:2310.03128**Yue Huang, Jiawen Shi, Yuan Li, et al.*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-metatool-benchmark-large-language-models/)
19. **[Factuality of Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/wang-2024-factuality-large-language-models-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, et al.*2024 · 77 citations*
20. **[Language Models in Dialogue: Conversational Maxims for Human-AI Interactions](https://md.research.elamur.ai/free/en/papers/miehling-2024-language-models-dialogue-conversational-maxims/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Erik Miehling, Manish Nagireddy, Prasanna Sattigeri, et al.*2024 · 23 citations*
21. **[DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning Graph](https://md.research.elamur.ai/free/en/papers/zhang-2024-darg-dynamic-evaluation-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhehao Zhang, Jiaao Chen, Diyi Yang*2024 · 17 citations*
22. **[Evaluating Large Language Models for Health-related Queries with Presuppositions](https://md.research.elamur.ai/free/en/papers/kaur-2024-evaluating-large-language-models-health/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Navreet Kaur, Monojit Choudhury, Danish Pruthi*2024 · 15 citations*
23. [**Evaluating LLMs' Divergent Thinking Capabilities for Scientific Idea Generation with Minimal Context arXiv:2412.17596**Kai Ruan, Xuan Wang, Jixiang Hong, et al.*2024 · 13 citations*](https://md.research.elamur.ai/free/en/papers/ruan-2024-evaluating-llms-divergent-thinking-capabilities/)
24. **[Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs](https://md.research.elamur.ai/free/en/papers/zhou-2026-flattery-motion-benchmarking-analyzing-sycophancy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenrui Zhou, Mohamed Hendy, Shu Yang, et al.*2026 · 6 citations*
25. **[Task Matters: Knowledge Requirements Shape LLM Responses to Context–Memory Conflict](https://md.research.elamur.ai/free/en/papers/sun-2026-task-matters-knowledge-requirements-shape/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaiser Sun, Fan Bai, Mark Dredze*2026 · 3 citations*
26. **[From Fact to Judgment: Investigating the Impact of Task Framing on LLM Conviction in Dialogue Systems](https://md.research.elamur.ai/free/en/papers/rabbani-2026-fact-judgment-investigating-impact-task/) [International Workshop on Spoken Dialogue System Technology](https://md.research.elamur.ai/free/en/venues/iwsds/)**Parisa Rabbani, Nimet Beyza Bozdag, Dilek Hakkani-Tur*2026 · 2 citations*
27. **[Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards?](https://md.research.elamur.ai/free/en/papers/molfetta-2026-sycophants-courtroom-are-llms-fragile/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lorenzo Molfetta, Alessio Cocchieri, Luca Ragazzi, et al.*2026 · 2 citations*
28. **[Sounding vs. Being an Expert: Disentangling Authority, Register and Cultural Impact in Sycophantic LLMs](https://md.research.elamur.ai/free/en/papers/maraia-2026-sounding-vs-being-expert-disentangling/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Gabriele Maraia, Fabio Massimo Zanzotto, Leonardo Ranaldi*2026 · 1 citation*
29. **[SycoBench-600: Measuring Sycophancy and Correction Selectivity in LLM Assistants](https://md.research.elamur.ai/free/en/papers/sinha-2026-sycobench-600-measuring-sycophancy-correction/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Debu Sinha*2026 · 1 citation*
30. **[Who Endorsed It? Measuring Authority Bias Across Expertise Levels in Language Models](https://md.research.elamur.ai/free/en/papers/mammen-2026-who-endorsed-it-measuring-authority/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Priyanka Mary Mammen, Emil Joswin, Shankar Venkitachalam*2026 · 1 citation*
31. **[Assertion-Conditioned Compliance: A Provenance-Aware Vulnerability in Multi-Turn Tool-Calling Agents](https://md.research.elamur.ai/free/en/papers/waqas-2026-assertion-conditioned-compliance-provenance-aware/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Daud Waqas, Aaryamaan Golthi, Erika Hayashida, et al.*2026*
32. **[DialDefer: A Framework for Detecting and Mitigating LLM Dialogic Deference](https://md.research.elamur.ai/free/en/papers/rabbani-2026-dialdefer-framework-detecting-mitigating-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Parisa Rabbani, Priyam Sahoo, Ruben Mathew, et al.*2026*
33. **[Evaluating Language Model Pluralism through In-the-wild Crowd Discussions](https://md.research.elamur.ai/free/en/papers/mundada-2026-evaluating-language-model-pluralism-through/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gagan Mundada, Rohan Surana, Nandhini Swaminathan, et al.*2026*
34. **[Feeling Right vs. Being Right: How AI Sycophancy Affects Value-Laden Deliberation](https://md.research.elamur.ai/free/en/papers/ryu-2026-feeling-right-vs-being-right/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jeongwoo Ryu, Soomin Kim, Jinsu Eun, et al.*2026*
35. **[I Understand How You Feel: Enhancing Deeper Emotional Support Through Multilingual Emotional Validation in Dialogue System](https://md.research.elamur.ai/free/en/papers/pang-2026-i-understand-how-you-feel/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Zi Haur Pang, Yahui Fu, Koji Inoue, et al.*2026*
36. **[Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency](https://md.research.elamur.ai/free/en/papers/xu-2026-illusions-confidence-diagnosing-llm-truthfulness/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoming Xu, Ningyuan Zhao, Yunzhi Yao, et al.*2026*
37. **[Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-Answering](https://md.research.elamur.ai/free/en/papers/naphade-2026-rational-synthesizers-or-heuristic-followers/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Atharv Naphade*2026*
38. **[Too Nice to Tell the Truth: Quantifying Agreeableness-Driven Sycophancy in Role-Playing Language Models](https://md.research.elamur.ai/free/en/papers/shah-2026-too-nice-tell-truth-quantifying/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Arya Shah, Deepali Mishra, Chaklam Silpasuwanchai*2026*
39. **[User-Assistant Bias in LLMs](https://md.research.elamur.ai/free/en/papers/pan-2026-user-assistant-bias-llms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xu Pan, Jingxuan Fan, Zidi Xiong, et al.*2026*
40. **[When Facts Change: Temporal Knowledge Conflict Resolution in LLMs](https://md.research.elamur.ai/free/en/papers/wallat-2026-when-facts-change-temporal-knowledge/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jonas Wallat, Wolfgang Nejdl, Sandipan Sikdar*2026*

67 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/wei-2023-simple-synthetic-data-reduces-sycophancy/) — Simple synthetic data reduces sycophancy in large language models