LURE
Model validator
Analyzing and mitigating object hallucination in large vision-language models to improve their accuracy and reliability.
[ICLR 2024] Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
136 stars
4 watching
5 forks
Language: Python
last commit: 10 months ago Related projects:
Repository | Description | Stars |
---|---|---|
| An implementation of a method to mitigate hallucinations in large language models using visual re-tracing | 28 |
| Debiasing techniques to minimize hallucinations in large visual language models | 75 |
| A method to correct hallucinations in multimodal large language models without requiring retraining | 617 |
| An image-context reasoning benchmark designed to challenge large vision-language models and help improve their accuracy | 259 |
| Evaluates and mitigates hallucinations in multimodal large language models | 82 |
| A research project focused on mitigating hallucinations in large multi-modal models by improving instruction tuning through robust training methods. | 262 |
| This project provides tools and frameworks to mitigate hallucinatory toxicity in visual instruction data, allowing researchers to fine-tune MLLM models on specific datasets. | 41 |
| An approach to reduce object hallucinations in large vision-language models by contrasting output distributions derived from original and distorted visual inputs | 222 |
| An evaluation framework for detecting object hallucinations in vision-language models | 187 |
| Improving multimodal hallucination mitigation in EOS decision-making by selectively supervising training data | 39 |
| This project provides an official PyTorch implementation of a method to interpret and edit vision-language representations to mitigate hallucinations in image captions. | 46 |
| Automates fine-grained hallucination detection in large language model outputs | 325 |
| Evaluates answers generated by large vision-language models to assess hallucinations | 27 |
| A framework for detecting hallucinations in large language models | 17 |
| Controlling object hallucination in large multimodal models | 28 |