LRV-Instruction
Hallucination mitigation
A research project focused on mitigating hallucinations in large multi-modal models by improving instruction tuning through robust training methods.
[ICLR'24] Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning
255 stars
11 watching
13 forks
Language: Python
last commit: 8 months ago chatgptevaluationevaluation-metricsfoundation-modelsgptgpt-4hallucinationiclriclr2024llamallavamultimodalobject-detectionprompt-engineeringvicunavisionvision-and-languagevqa
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 |
 yuezih/less-is-more | Improving multimodal hallucination mitigation in EOS decision-making by selectively supervising training data | 31 |
 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 |
 x-plug/mplug-halowl | Evaluates and mitigates hallucinations in multimodal large language models | 79 |
 bradyfu/woodpecker | A method to correct hallucinations in multimodal large language models during text generation | 611 |
 tianyi-lab/hallusionbench | An image-context reasoning benchmark designed to challenge large vision-language models and help improve their accuracy | 243 |
 yfzhang114/llava-align | Debiasing techniques to minimize hallucinations in large visual language models | 71 |
 yiyangzhou/lure | Analyzing and mitigating object hallucination in large vision-language models to improve their accuracy and reliability. | 134 |
 assafbk/mocha_code | A unified framework and benchmark for detecting and mitigating hallucinations in open-vocabulary image captioning models | 12 |
 billchan226/halc | An implementation of an object hallucination reduction method using a PyTorch framework and various decoding algorithms. | 69 |
 opendatalab/ha-dpo | A framework to improve large language model performance by mitigating hallucination effects through data and optimization techniques. | 65 |
 vectara/hallucination-leaderboard | Evaluates and compares the performance of large language models in generating hallucinations during document summarization. | 1,236 |
 openmoss/halluqa | An evaluation framework for assessing the performance of large language models on question-answering tasks with hallucination detection | 109 |
| fuxiaoliu/mmc | Develops a large-scale dataset and benchmark for training multimodal chart understanding models using large language models. | 84 |