{"data":{"slug":"luckyyysta-awesome-llm-hallucination","name":"Awesome-LLM-hallucination","tagline":"A Survey on Hallucination in Large Language Models","github_url":"https://github.com/LuckyyySTA/Awesome-LLM-hallucination","owner":"LuckyyySTA","repo":"Awesome-LLM-hallucination","owner_avatar_url":"https://avatars.githubusercontent.com/u/41715836?v=4","primary_language":null,"stars":337,"forks":27,"topics":[],"archived":false,"github_pushed_at":"2024-03-11T12:49:15+00:00","maintenance_label":"Dormant","url":"https://www.graphcanon.com/tools/luckyyysta-awesome-llm-hallucination","markdown_url":"https://www.graphcanon.com/tools/luckyyysta-awesome-llm-hallucination.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/luckyyysta-awesome-llm-hallucination","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=luckyyysta-awesome-llm-hallucination","description":"LLM hallucination paper list","homepage_url":null,"license":"MIT","open_issues":5,"watchers":5,"ai_summary":"Provides a curated list and analysis of hallucination-related papers in the context of LLMs, including categorization by causes, detection, mitigation, challenges, and open questions.","readme_excerpt":"
\n

\nA Survey on Hallucination in Large Language Models: \nPrinciples, Taxonomy, Challenges, and Open Questions\n

\n
\n\n
\nLei Huang1∗,\nWeijiang Yu2∗,\nWeitao Ma1,\nWeihong Zhong1,\nZhangyin Feng1,\nHaotian Wang1,\nQianglong Chen2,\nWeihua Peng2,\nXiaocheng Feng1†,\nBing Qin1,\nTing Liu1\n
\n\n
\n1Harbin Institute of Technology, Harbin, China\n
\n
\n2Huawei Inc., Shenzhen, China\n
\n\nThis repository contains the resources for our survey paper.\n\n\n\n

\n \"taxonomy\"/\n
\n The main content flow and categorization of this survey.\n

\n\n## :tada: Updates\n- 2023/11/09 The first version of our paper is available on [arXiv](https://arxiv.org/abs/2311.05232)\n\n## :page_with_curl: Papers\n\nWe have surveyed papers related to Large Language Model hallucination. This includes **related survey or analytical papers**, **hallucination causes**, **hallucination detection and benchmarks**, **hallucination mitigation**, as well as **challenges and open questions** in the field.\n\n### :memo:Related Survey / Analytical Papers\n\n> We provide a curated list of survey papers that delve into the topic of hallucination in LLMs.\n\n#### Related Survey papers\n\n1. **Survey of Hallucination in Natural Language Generation** `ACM Computing Surveys 2023 `\n\n *Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Wenliang Dai, Andrea Madotto, Pascale Fung* [[paper]](https://arxiv.org/abs/2202.03629) 2022.02\n\n2. **Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment** `arXiv 2023`\n\n *Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo, Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, Hang Li* [[paper]](https://arxiv.org/abs/2308.05374) 2023.08\n\n3. **Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models** `arXiv 2023`\n\n *Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, Shuming Shi* [[paper]](https://arxiv.org/abs/2309.01219) 2023.09\n\n4. **Cognitive Mirage: A Review of Hallucinations in Large Language Models** `arXiv 2023`\n\n *Hongbin Ye, Tong Liu, Aijia Zhang, Wei Hua, Weiqiang Jia* [[paper]](https://arxiv.org/abs/2309.06794) 2023.09\n\n5. **A Survey of Hallucination in Large Foundation Models** `arXiv 2023`\n\n *Vipula Rawte, Amit Sheth, Amitava Das* [[paper]](https://arxiv.org/abs/2309.05922) 2023.09\n\n6. **Augmenting LLMs with Knowledge: A survey on hallucination prevention** `arXiv 2023`\n\n *Konstantinos Andriopoulos, Johan Pouwelse* [[paper]](https://arxiv.org/abs/2309.16459) 2023.09\n\n7. **Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity** `arXiv 2023`\n\n *Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, Xiangru Tang, Tianhang Zhang, Cheng Jiayang, Yunzhi Yao, Wenyang Gao, Xuming Hu, Zehan Qi, Yidong Wang, Linyi Yang, Jindong Wang, Xing Xie, Zheng Zhang, Yue Zhang* [[paper]](https://arxiv.org/abs/2310.07521) 2023.10\n\n8. **Insights into Classifying and Mitigating LLMs' Hallucinations** `AIxIA 2023`\n\n *Alessandro Bruno, Pier Luigi Mazzeo, Aladine Chetouani, Marouane Tliba, Mohamed Amine Kerkouri* [[paper]](https://arxiv.org/abs/2311.08117) 2023.11\n\n#### Related Analytical papers\n\n1. **A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity** `arXiv 2023`\n\n *Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, Quyet V. Do, Yan Xu, Pascale Fung* [[paper]](https://arxiv.org/abs/230","github_created_at":"2023-07-12T02:56:59+00:00","created_at":"2026-07-11T10:30:32.180715+00:00","updated_at":"2026-07-12T06:34:15.2829+00:00","categories":[{"slug":"evaluation-observability","name":"Evaluation & Observability","url":"https://www.graphcanon.com/categories/evaluation-observability","markdown_url":"https://www.graphcanon.com/categories/evaluation-observability.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/evaluation-observability"}],"tags":[{"slug":"hallucination","name":"hallucination"},{"slug":"large-language-models","name":"large-language-models"},{"slug":"llm","name":"llm"},{"slug":"survey","name":"survey"}],"trust":{"provenance":{"is_fork":false,"github_id":665353423,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T10:30:32.640Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":851,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T10:30:39.404Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T11:28:37.319Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-07-11T11:28:37.319Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["The exact language used by the repository is unknown, as no specific programming languages are listed."]},"constraints":null,"when_to_use":["- When you need detailed categorizations by causes, detection methods, and mitigation strategies for LLM hallucinations.","- If your project requires up-to-date insights on the latest research in mitigating hallucination challenges within LLMs.","- For a comprehensive understanding of principles and taxonomy around hallucination in LLMs as documented in recent academic papers."],"when_not_to_use":["- Avoid using this resource for practical, hands-on tools or code that helps mitigate hallucinations directly (it's primarily informative).","- Do not use if you are looking for real-time diagnostic software for identifying and correcting LLM hallucination mistakes in live applications.","- This tool is not suitable as a standalone guide for implementing mitigation techniques within your own large language models; it lacks detailed technical instructions."],"source":"enrich:decision_facts","observed_at":"2026-07-11T11:29:16.141Z"},"constraint_facets":null,"decision_summary":[{"label":"Requirements","value":"The exact language used by the repository is unknown, as no specific programming languages are listed."},{"label":"Adopt for","value":"Awesome-LLM-hallucination stands out as a resource dedicated to the in-depth analysis of hallucination phenomena within Large Language Models (LLMs). Its curated list and categorization make it distinct from other tools,"},{"label":"License detail","value":"MIT"}]}}