HalluQA
Hallucination detector
An evaluation framework for assessing the performance of large language models on question-answering tasks with hallucination detection
Dataset and evaluation script for "Evaluating Hallucinations in Chinese Large Language Models"
111 stars
5 watching
4 forks
Language: Python
last commit: over 2 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
 openkg-org/easydetect | A framework to detect and mitigate hallucinations in multimodal large language models | 48 |
 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 |
 amazon-science/refchecker | Automates fine-grained hallucination detection in large language model outputs | 325 |
 opendatalab/ha-dpo | A framework to improve large language model performance by mitigating hallucination effects through data and optimization techniques. | 73 |
 assafbk/mocha_code | A unified framework and benchmark for detecting and mitigating hallucinations in open-vocabulary image captioning models | 13 |
 x-plug/mplug-halowl | Evaluates and mitigates hallucinations in multimodal large language models | 82 |
 bradyfu/woodpecker | A method to correct hallucinations in multimodal large language models without requiring retraining | 617 |
 1zhou-wang/memvr | An implementation of a method to mitigate hallucinations in large language models using visual re-tracing | 28 |
 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 |
 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 |
 fuxiaoliu/lrv-instruction | A research project focused on mitigating hallucinations in large multi-modal models by improving instruction tuning through robust training methods. | 262 |
 bcdnlp/faithscore | Evaluates answers generated by large vision-language models to assess hallucinations | 27 |
 tianyi-lab/hallusionbench | An image-context reasoning benchmark designed to challenge large vision-language models and help improve their accuracy | 259 |
 masaiahhan/correlationqa | An investigation into the relationship between misleading images and hallucinations in large language models | 8 |