mPLUG-HalOwl
Hallucination tester
Evaluates and mitigates hallucinations in multimodal large language models
mPLUG-HalOwl: Multimodal Hallucination Evaluation and Mitigating
82 stars
1 watching
2 forks
Language: Python
last commit: about 1 year ago benchmarkcontrastive-learninghallucinationsmllmmultimodal-hallucinationmultimodal-large-language-models
Related projects:
Repository | Description | Stars |
---|---|---|
| A method to correct hallucinations in multimodal large language models without requiring retraining | 617 |
| An LLM-free benchmark suite for evaluating MLLMs' hallucination capabilities in various tasks and dimensions | 98 |
| A framework for detecting hallucinations in large language models | 17 |
| Evaluating and improving large multimodal models through in-context learning | 21 |
| A research project focused on mitigating hallucinations in large multi-modal models by improving instruction tuning through robust training methods. | 262 |
| An implementation of a method to mitigate hallucinations in large language models using visual re-tracing | 28 |
| Improves the performance of large language models by intervening in their internal workings to reduce hallucinations | 83 |
| An image-context reasoning benchmark designed to challenge large vision-language models and help improve their accuracy | 259 |
| Automates fine-grained hallucination detection in large language model outputs | 325 |
| 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 |
| An implementation of an object hallucination reduction method using a PyTorch framework and various decoding algorithms. | 72 |
| Analyzing and mitigating object hallucination in large vision-language models to improve their accuracy and reliability. | 136 |
| Improving multimodal hallucination mitigation in EOS decision-making by selectively supervising training data | 39 |
| Debiasing techniques to minimize hallucinations in large visual language models | 75 |
| An evaluation framework for assessing the performance of large language models on question-answering tasks with hallucination detection | 111 |