Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
📖 A curated list of resources dedicated to hallucination of multimodal large language models (MLLM).
| Date | Stars |
|---|---|
| 2026-07-31 | 1033 |
| 2026-08-06 | 1035 |
Today
+2 stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# Awesome MLLM Hallucination [](https://github.com/sindresorhus/awesome) <!-- omit in toc --> ## [Hallucination of Multimodal Large Language Models: A Survey](https://arxiv.org/abs/2404.18930) ### :star: News! The Latest & Most Comprehensive Survey on MLLM Hallucination Just Got Better! Thrilled to unveil a **major update** to our landmark survey on MLLM (LVLM) Hallucination—packed with groundbreaking insights from 2024-2025! 🔥🔥🔥 [Paper (40 pages, 228 references)](https://arxiv.org/abs/2404.18930). This is your ultimate guide to navigating the fast-evolving landscape of MLLM hallucination. Whether you’re a researcher or practitioner, **bookmark this handbook**! 🔍 Key observations in the update: ✅ There are **deeper yet diverse analysis** on the root cause of hallucination. ✅ **Training-free** mitigating solutions are getting popular, probably thanks to its resource-friendly property. ✅ **Contrastive decoding** becomes a cornerstone technique! Various ideas have been developed based on it. ✅ **RL-based methods** is gaining momentum, and we expect more methods on this track in the near future. ✅ More fresh angles to mitigate hallucination: Visual prompting, RAG, rationale reasoning, generative feedback, and so on. <p align="center"> <img src="assets/tax.png" alt="TAX" style="display: block; margin: 0 auto;" /> </p> ---- This is a repository for organizing papres, codes and other resources related to hallucination of Multimodal Large Language Models (MLLM), or called Large Vision-Language Models (LVLM). Hallucination in LLM usually refers to the phenomenon that the generated content is nonsensical or unfaithful to the provided source content, such as violation of input instruction, or containing factual errors, etc. In the context of MLLM, hallucination refers to the phenomenon that the generated text is semantically coherent but inconsistent with the given visual content. The community has been constantly making progress on analyzing, detecting, and mitigating hallucination in MLLM. #### :books: How to read? The main contribution of a specific paper is proposing either a new hallucination benchmark (metric) or proposing a hallucination mitigation method. The analysis and detection of hallucination are only part of the whole paper, serving as the basis of evaluation and mitigation. Therefore, we divide the papers into two categories: **hallucination evaluation & analysis ** and **hallucination mitigation**. In each category, the paper are listd in an order **from new to old**. Note that there might be some duplicated papers in the two categories. Those papers contain both evaluation benchmark and mitigation method. #### :high_brightness: This project is still on-going, pull requests are welcomed!! If you have any suggestions (missing papers, new papers, key researchers or typos), please feel free to edit and pull a request. Just letting us know the title of papers can also be a great contribution to us. You can do this by open issue or contact us directly via email. #### :star: If you find this repo useful, please star it!!! ## Table of Contents <!-- omit in toc --> - [Hallucination Survey](#hallucination-survey) - [Hallucination Evaluation & Analysis ](#hallucination-evaluation--analysis) - [Hallucination Mitigation](#hallucination-mitigation) ### Hallucination Survey + [Hallucination of Multimodal Large Language Models: A Survey](https://arxiv.org/abs/2404.18930) (Apr. 30, 2024) [](https://arxiv.org/abs/2404.18930) + [A Survey on Hallucination in Large Vision-Language Models](https://arxiv.org/abs/2402.00253) (Feb. 01, 2024) [](https://arxiv.org/abs/2402.00253) ### Hallucination Evaluation & Analysis + **HumbleBench** [Measuring Epistemic Humility in Multimodal Large Language
Excerpt of 67,505 characters
Read on GitHub
52
4
2
1
1
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7ea9219ad4a3813e, desc:multimodal