新闻
ReWEIGH the Evidence: Calibrating Token-Level Ordinal Visual Evidence to Mitigate Hallucinations in Large Vision-Language Models
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- ReWEIGH, a training-free decoding method that reduces hallucinations in vision-language models by 21.3% using token-level visual evidence calibration.
- 为何重要
- Engineers deploying large vision-language models who need to reduce false object mentions while maintaining overall model performance and descriptive quality.
- 注意
- Method adds 1.33% latency per token and requires unlabeled image data for reference estimation; effectiveness may vary across different model architectures.
收听本摘要
- language model
- token
- hallucinat
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