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About this paper
Yuechen Zhang, Shengju Qian, Bohao Peng, Shu Liu, Jiaya Jia
Prompt Highlighter introduces a training-free inference method that provides interactive, token-level control for multi-modal Large Language Models. This approach allows users to guide generation by highlighting specific input elements, leading to improved output fidelity, reduced hallucination, and quantitative gains such as up to 2.5 points on MMBench.
Key insights
- Fine-grained, token-level control over multi-modal LLMs is achievable via a plug-and-play inference method without requiring additional training or fine-tuning of base models.
- Combining logit reweighting with direct attention modification effectively steers multi-modal LLM generation towards highlighted input contexts.
- Explicitly guiding the model's focus on reliable input contexts through interactive highlighting can significantly mitigate 'hallucinations' and improve output trustworthiness.
Summary by alphaXiv.
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