Summary of Pay Attention to What Matters, by Pedro Luiz Silva et al.
Pay Attention to What Matters
by Pedro Luiz Silva, Antonio de Domenico, Ali Maatouk, Fadhel Ayed
First submitted to arxiv on: 19 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 paper introduces a method called GUIDE, which boosts attention scores in instruction tokens for Large Language Models (LLMs). To achieve this, the authors propose Influence, a new metric that tracks how user instructions influence transformer layers’ outputs. The results demonstrate that GUIDE improves instruction-following accuracy by 29.4-60.4% compared to natural prompting and Supervised Fine-Tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps Large Language Models (LLMs) better understand what they’re supposed to do, which is important because they can’t always get it right on their own. The authors have a new way to help LLMs pay attention to the instructions given to them, and this makes them more accurate at following those instructions. |
Keywords
» Artificial intelligence » Attention » Fine tuning » Prompting » Supervised » Transformer
