PAI
Hallucination fixer
Improves the performance of large language models by intervening in their internal workings to reduce hallucinations
[ECCV 2024] Paying More Attention to Image: A Training-Free Method for Alleviating Hallucination in LVLMs
67 stars
2 watching
2 forks
Language: Python
last commit: 16 days ago Related projects:
| Repository | Description | Stars |
|---|---|---|
 1zhou-wang/memvr | An implementation of a method to mitigate hallucinations in large language models using visual re-tracing | 27 |
 bradyfu/woodpecker | A method to correct hallucinations in multimodal large language models during text generation | 611 |
 opendatalab/ha-dpo | A framework to improve large language model performance by mitigating hallucination effects through data and optimization techniques. | 65 |
 billchan226/halc | An implementation of an object hallucination reduction method using a PyTorch framework and various decoding algorithms. | 69 |
 fuxiaoliu/lrv-instruction | A research project focused on mitigating hallucinations in large multi-modal models by improving instruction tuning through robust training methods. | 255 |
 yfzhang114/llava-align | Debiasing techniques to minimize hallucinations in large visual language models | 71 |
 x-plug/mplug-halowl | Evaluates and mitigates hallucinations in multimodal large language models | 79 |
 amazon-science/refchecker | Automates fine-grained hallucination detection in large language model outputs | 302 |
 tianyi-lab/hallusionbench | An image-context reasoning benchmark designed to challenge large vision-language models and help improve their accuracy | 243 |
 nickjiang2378/vl-interp | This project provides an official PyTorch implementation of a method to interpret and edit vision-language representations to mitigate hallucinations in image captions. | 31 |
 junyangwang0410/haelm | A framework for detecting hallucinations in large language models | 17 |
 yuqifan1117/hallucidoctor | 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 |
 assafbk/mocha_code | A unified framework and benchmark for detecting and mitigating hallucinations in open-vocabulary image captioning models | 12 |
 bcdnlp/faithscore | Evaluates answers generated by large vision-language models to assess hallucinations | 25 |
 damo-nlp-sg/vcd | An approach to reduce object hallucinations in large vision-language models by contrasting output distributions derived from original and distorted visual inputs | 209 |