mocha_code
Hallucination detection and mitigation
A unified framework and benchmark for detecting and mitigating hallucinations in open-vocabulary image captioning models
Code Repo for the paper 'Mitigating Open-Vocabulary Caption Hallucinations'
13 stars
3 watching
0 forks
Language: Python
last commit: 3 months ago Related projects:
Repository | Description | Stars |
---|---|---|
amazon-science/refchecker | Automates fine-grained hallucination detection in large language model outputs | 325 |
bradyfu/woodpecker | A method to correct hallucinations in multimodal large language models without requiring retraining | 617 |
openmoss/halluqa | An evaluation framework for assessing the performance of large language models on question-answering tasks with hallucination detection | 111 |
junyangwang0410/haelm | A framework for detecting hallucinations in large language models | 17 |
rucaibox/pope | An evaluation framework for detecting object hallucinations in vision-language models | 187 |
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 | 222 |
masaiahhan/correlationqa | An investigation into the relationship between misleading images and hallucinations in large language models | 8 |
1zhou-wang/memvr | An implementation of a method to mitigate hallucinations in large language models using visual re-tracing | 28 |
fuxiaoliu/lrv-instruction | A research project focused on mitigating hallucinations in large multi-modal models by improving instruction tuning through robust training methods. | 262 |
yfzhang114/llava-align | Debiasing techniques to minimize hallucinations in large visual language models | 75 |
x-plug/mplug-halowl | Evaluates and mitigates hallucinations in multimodal large language models | 82 |
openkg-org/easydetect | A framework to detect and mitigate hallucinations in multimodal large language models | 48 |
tianyi-lab/hallusionbench | An image-context reasoning benchmark designed to challenge large vision-language models and help improve their accuracy | 259 |
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. | 46 |
bcdnlp/faithscore | Evaluates answers generated by large vision-language models to assess hallucinations | 27 |