Counterfactual-Inception
Hallucination mitigator
An implementation of a method to reduce hallucination in large multi-modal models by integrating counterfactual thinking through generated keywords.
Official PyTorch Implementation for the "What if...?: Thinking Counterfactual Keywords Helps to Mitigate Hallucination in Large Multi-modal Models" paper (EMNLP Findings 2024).
15 stars
0 watching
1 forks
Language: Python
last commit: 5 months ago Related projects:
Repository | Description | Stars |
---|---|---|
| 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 |
| 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 |
| A method to correct hallucinations in multimodal large language models without requiring retraining | 617 |
| A unified framework and benchmark for detecting and mitigating hallucinations in open-vocabulary image captioning models | 13 |
| Evaluates and mitigates hallucinations in multimodal large language models | 82 |
| An implementation of an object hallucination reduction method using a PyTorch framework and various decoding algorithms. | 72 |
| A framework to improve large language model performance by mitigating hallucination effects through data and optimization techniques. | 73 |
| This project provides an official PyTorch implementation of a method to interpret and edit vision-language representations to mitigate hallucinations in image captions. | 46 |
| A framework for detecting hallucinations in large language models | 17 |
| Compares performance of large language models on generating coherent summaries from short documents | 1,281 |
| Debiasing techniques to minimize hallucinations in large visual language models | 75 |
| An image-context reasoning benchmark designed to challenge large vision-language models and help improve their accuracy | 259 |
| A framework to detect and mitigate hallucinations in multimodal large language models | 48 |