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arXiv · 2409.19001

Pay Attention to What Matters

Pedro Luiz Silva·Antonio de Domenico·Ali Maatouk·Fadhel Ayed
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Abstract

Despite the remarkable success of Large Language Models (LLMs), they still exhibit a limited capability to align their outputs to the user instructions. In this work, we introduce a simple and effective method, which we name GUIDE, that mechanistically increases attention scores in instruction tokens. To support this operation, we present Influence, a novel metric that highlights how the user's instructions propagate through the transformer layers and impact the LLM output. Our results show that GUIDE improves the accuracy of following instructions 29.4 % to 60.4%, outperforming natural prompting alternatives and Supervised Fine-Tuning up to 1M tokens.

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Pedro Luiz Silva, Antonio de Domenico, Ali Maatouk, Fadhel Ayed. 2024-09-19. Pay Attention to What Matters. https://arxiv.org/abs/2409.19001

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