Awesome-LLM-hallucination
A Survey on Hallucination in Large Language Models
GraphCanon updated 2w · GitHub synced 2w
Decision brief
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,
Good fit when
- - 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.
Avoid when
- - 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.
- Requirements:
- The exact language used by the repository is unknown, as no specific programming languages are listed.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (877d since push)
- As of 2w
- Provenance
- Not a fork · Personal account
- As of 2w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/LuckyyySTA/Awesome-LLM-hallucinationSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
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.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 6, 2026)
1. **A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity** `arXiv 2023`Source link
Tags
README
A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions
This repository contains the resources for our survey paper.
The main content flow and categorization of this survey.
:tada: Updates
- 2023/11/09 The first version of our paper is available on arXiv
:page_with_curl: Papers
We 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.
:memo:Related Survey / Analytical Papers
We provide a curated list of survey papers that delve into the topic of hallucination in LLMs.
Related Survey papers
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Survey of Hallucination in Natural Language Generation
ACM Computing Surveys 2023Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Wenliang Dai, Andrea Madotto, Pascale Fung [paper] 2022.02
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Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
arXiv 2023Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo, Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, Hang Li [paper] 2023.08
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Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models
arXiv 2023Yue 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] 2023.09
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Cognitive Mirage: A Review of Hallucinations in Large Language Models
arXiv 2023Hongbin Ye, Tong Liu, Aijia Zhang, Wei Hua, Weiqiang Jia [paper] 2023.09
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A Survey of Hallucination in Large Foundation Models
arXiv 2023Vipula Rawte, Amit Sheth, Amitava Das [paper] 2023.09
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Augmenting LLMs with Knowledge: A survey on hallucination prevention
arXiv 2023Konstantinos Andriopoulos, Johan Pouwelse [paper] 2023.09
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Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
arXiv 2023Cunxiang 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] 2023.10
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Insights into Classifying and Mitigating LLMs' Hallucinations
AIxIA 2023Alessandro Bruno, Pier Luigi Mazzeo, Aladine Chetouani, Marouane Tliba, Mohamed Amine Kerkouri [paper] 2023.11
Related Analytical papers
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A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity
arXiv 2023Yejin 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
For agents
This page has a .md twin and JSON over the API.