rese1f / Awesome-MLLM-Hallucination

📖 A curated list of resources dedicated to hallucination of multimodal large language models (MLLM).

Awesome MLLM Hallucination

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.

📚 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 paper into two categories: hallucination evaluation 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.

🔆 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.

⭐ If you find this repo useful, please star it!!!

Table of Contents

• Hallucination Benchmarks
• Hallucination Mitigation

Hallucination Benchmarks

• MMVP Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs (Jan. 11, 2024)

• MOCHa (OpenCHAIR) MOCHa: Multi-Objective Reinforcement Mitigating Caption Hallucinations (Dec. 06, 2023)

• FGHE Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites (Dec. 04, 2023)

• MERLIM Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models (Dec. 03, 2023)

• CCEval HallE-Switch: Controlling Object Hallucination in Large Vision Language Models (Dec. 03, 2023)

• HallusionBench HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination & Visual Illusion in Large Vision-Language Models (Nov. 28, 2023)

• RAH-Bench Mitigating Hallucination in Visual Language Models with Visual Supervision (Nov. 27, 2023)

• AMBER An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation (Nov. 13, 2023)

• Bingo Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges (Nov. 7, 2023)

• FAITHSCORE FAITHSCORE: Evaluating Hallucinations in Large Vision-Language Models (Nov. 2, 2023)

• HaELM Evaluation and Analysis of Hallucination in Large Vision-Language Models (Oct. 10, 2023)

• NOPE Negative Object Presence Evaluation (NOPE) to Measure Object Hallucination in Vision-Language Models (Oct. 9, 2023)

• LRV (GAVIE) Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning (Sep., 29 2023)
  

• MMHal-Bench Aligning Large Multimodal Models with Factually Augmented RLHF (Sep. 25, 2023)
  

• POPE Evaluating Object Hallucination in Large Vision-Language Models (EMNLP 2023)

• CHAIR Object Hallucination in Image Captioning (EMNLP 2018)

Hallucination Mitigation

• HACL Hallucination Augmented Contrastive Learning for Multimodal Large Language Model (Dec. 12, 2023)

• MOCHa (OpenCHAIR) MOCHa: Multi-Objective Reinforcement Mitigating Caption Hallucinations (Dec. 06, 2023)

• FGHE Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites (Dec. 04, 2023)

• HallE-Switch HallE-Switch: Controlling Object Hallucination in Large Vision Language Models (Dec. 03, 2023)

• VCD RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback (Dec. 01, 2023)

• OPERA OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation (Nov. 29, 2023)

• VCD Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding (Nov. 28, 2023)

• HA-DPO Beyond Hallucinations: Enhancing LVLMs through Hallucination-Aware Direct Preference Optimization (Nov. 28, 2023)

• RAH-Bench Mitigating Hallucination in Visual Language Models with Visual Supervision (Nov. 27, 2023)

• HalluciDoctor HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data (Nov. 22, 2023)

• Volcano Volcano: Mitigating Multimodal Hallucination through Self-Feedback Guided Revision (Nov. 14, 2023)

• Woodpecker Woodpecker: Hallucination Correction for Multimodal Large Language Models (Oct. 24, 2023)
  

• LURE Analyzing and Mitigating Object Hallucination in Large Vision-Language Models (Oct. 1, 2023)
  

• LRV-Instruction Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning (Sep. 29, 2023)
  

• LLaVA-RLHF Aligning Large Multimodal Models with Factually Augmented RLHF (Sep. 25, 2023)
  

• VIGC VIGC: Visual Instruction Generation and Correction (Sep. 11, 2023)

• HalDectect Detecting and Preventing Hallucinations in Large Vision Language Models (Aug. 18, 2023)