FAITHSCORE

Hallucination detector

Evaluates answers generated by large vision-language models to assess hallucinations

GitHub

25 stars
2 watching
4 forks
Language: Python
last commit: 13 days ago

Related projects:

Repository Description Stars
amazon-science/refchecker Automates fine-grained hallucination detection in large language model outputs 302
bradyfu/woodpecker A method to correct hallucinations in multimodal large language models during text generation 611
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
junyangwang0410/haelm A framework for detecting hallucinations in large language models 17
tianyi-lab/hallusionbench An image-context reasoning benchmark designed to challenge large vision-language models and help improve their accuracy 243
rucaibox/pope An evaluation framework for detecting object hallucinations in vision-language models 179
yiyangzhou/lure Analyzing and mitigating object hallucination in large vision-language models to improve their accuracy and reliability. 134
x-plug/mplug-halowl Evaluates and mitigates hallucinations in multimodal large language models 79
openkg-org/easydetect A framework to detect and mitigate hallucinations in multimodal large language models 48
assafbk/mocha_code A unified framework and benchmark for detecting and mitigating hallucinations in open-vocabulary image captioning models 12
yfzhang114/llava-align Debiasing techniques to minimize hallucinations in large visual language models 71
openmoss/halluqa An evaluation framework for assessing the performance of large language models on question-answering tasks with hallucination detection 109
lalbj/pai Improves the performance of large language models by intervening in their internal workings to reduce hallucinations 67
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
junyangwang0410/amber An LLM-free benchmark suite for evaluating MLLMs' hallucination capabilities in various tasks and dimensions 93