Pay Attention to What Matters
<|reference_start|>Pay Attention to What Matters: 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.<|reference_end|>
@article{silva2024pay,
    title={Pay Attention to What Matters},
    author={Pedro Luiz Silva, Antonio de Domenico, Ali Maatouk, Fadhel Ayed},
    journal={arXiv preprint arXiv:2409.19001},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19001},
    primaryClass={cs.CL cs.AI}
}