elder-plinius/CL4R1T4S
CL4R1T4S is a project that provides transparency by collecting and sharing the hidden system prompts and guidelines used by major AI models and agents to promote trust and understanding of AI behavior.
Awesome AI Agents › LLM Research Repositories
showlab/Awesome-MLLM-Hallucination
The project "Awesome MLLM Hallucination" is a curated repository dedicated to the study and mitigation of hallucination phenomena in Multimodal Large Language Models (MLLM), also known as Large Vision-Language Models (LVLM). Hallucination in this context refers to the generation of text by these models that, while semantically coherent, is inconsistent or unfaithful to the visual content provided as input. This repository serves as a comprehensive resource hub, organizing a wide range of academic papers, codebases, and other materials that focus on analyzing, detecting, and mitigating hallucinations in MLLMs. The repository highlights a major survey paper that provides an extensive overview of the state-of-the-art research on MLLM hallucination, including recent advancements from 2024 to 2025. Key insights from this survey include deeper analysis of hallucination root causes, the rise of training-free mitigation methods, the importance of contrastive decoding techniques, and the growing use of reinforcement learning-based approaches. Additionally, the repository categorizes research contributions into two main areas: hallucination evaluation and analysis, and hallucination mitigation. This classification helps users navigate the literature based on whether the focus is on benchmarking and understanding hallucination or on developing methods to reduce it. The project is actively maintained and encourages community contributions through pull requests and issue reporting. It also provides a structured table of contents linking to various influential papers and benchmarks, covering topics such as medical hallucination, temporal hallucinations in video understanding, and visual prompting techniques. Overall, this repository is an invaluable resource for researchers and practitioners interested in improving the reliability and accuracy of multimodal language models by addressing hallucination challenges.
https://github.com/showlab/Awesome-MLLM-Hallucination
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