Summary of Dopra: Decoding Over-accumulation Penalization and Re-allocation in Specific Weighting Layer, by Jinfeng Wei et al.
DOPRA: Decoding Over-accumulation Penalization and Re-allocation in Specific Weighting Layer
by Jinfeng Wei, Xiaofeng Zhang
First submitted to arxiv on: 21 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces DOPRA, a novel approach to mitigate hallucinations in multi-modal large language models (MLLMs). Unlike existing solutions, DOPRA addresses hallucinations without additional resources by decoding specific weighted layer penalties and redistribution. It is grounded in insights into the intrinsic mechanisms controlling hallucinations within MLLMs, particularly the tendency to over-rely on summary tokens in the self-attention matrix. To counteract this over-reliance, DOPRA employs a strategy of weighted overlay penalties and redistribution in specific layers during decoding. Additionally, it includes a retrospective allocation process that reallocates token selection to better align with image content, reducing hallucinatory descriptions in auto-generated captions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to fix a problem with big language models that sometimes make things up when describing pictures. It’s called “hallucination” and it happens because the models rely too much on certain words or ideas instead of looking at the picture. The new method, called DOPRA, makes the models look more carefully at the picture and use the right words to describe what they see. |
Keywords
» Artificial intelligence » Hallucination » Multi modal » Self attention » Token
