Summary of Pear: Position-embedding-agnostic Attention Re-weighting Enhances Retrieval-augmented Generation with Zero Inference Overhead, by Tao Tan et al.
PEAR: Position-Embedding-Agnostic Attention Re-weighting Enhances Retrieval-Augmented Generation with Zero Inference Overhead
by Tao Tan, Yining Qian, Ang Lv, Hongzhan Lin, Songhao Wu, Yongbo Wang, Feng Wang, Jingtong Wu, Xin Lu, Rui Yan
First submitted to arxiv on: 29 Sep 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 proposed Position-Embedding-Agnostic attention Re-weighting (PEAR) method enhances the context awareness of large language models (LLMs) with zero inference overhead. The approach detects heads that suppress context awareness and re-weights their outputs to minimize loss on a proxy task. This results in optimized coefficients that reduce the tendency to suppress retrieval-augmented generation (RAG) performance. PEAR outperforms competitive baselines in accuracy and efficiency across various RAG tasks, introducing zero additional inference overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can now better understand the context of web searches without slowing down or using extra memory! The problem is that these models don’t always get the bigger picture, which makes them less good at searching for specific information. To fix this, scientists came up with a new way to make these models smarter about context without making them slower or taking up more space. This new method, called PEAR, makes sure the model doesn’t miss important details and gets better results. It’s like giving the model a special pair of glasses that helps it see things more clearly! |
Keywords
» Artificial intelligence » Attention » Embedding » Inference » Rag » Retrieval augmented generation
