HalluciDoctor
Data processing framework
This project provides tools and frameworks to mitigate hallucinatory toxicity in visual instruction data, allowing researchers to fine-tune MLLM models on specific datasets.
HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data (Accepted by CVPR 2024)
41 stars
1 watching
0 forks
Language: Python
last commit: about 2 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
 fuxiaoliu/lrv-instruction | A research project focused on mitigating hallucinations in large multi-modal models by improving instruction tuning through robust training methods. | 262 |
 opendatalab/ha-dpo | A framework to improve large language model performance by mitigating hallucination effects through data and optimization techniques. | 73 |
 bradyfu/woodpecker | A method to correct hallucinations in multimodal large language models without requiring retraining | 617 |
 x-plug/mplug-halowl | Evaluates and mitigates hallucinations in multimodal large language models | 82 |
 1zhou-wang/memvr | An implementation of a method to mitigate hallucinations in large language models using visual re-tracing | 28 |
 yiyangzhou/lure | Analyzing and mitigating object hallucination in large vision-language models to improve their accuracy and reliability. | 136 |
 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 | 259 |
 billchan226/halc | An implementation of an object hallucination reduction method using a PyTorch framework and various decoding algorithms. | 72 |
 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 |
 yfzhang114/llava-align | Debiasing techniques to minimize hallucinations in large visual language models | 75 |
 lalbj/pai | Improves the performance of large language models by intervening in their internal workings to reduce hallucinations | 83 |
 sarababakn/mfcl-neurips23 | An approach to mitigating catastrophic forgetting in federated class incremental learning for vision tasks using a generative model and data-free methods | 15 |
 openmoss/halluqa | An evaluation framework for assessing the performance of large language models on question-answering tasks with hallucination detection | 111 |
 bcdnlp/faithscore | Evaluates answers generated by large vision-language models to assess hallucinations | 27 |