Pay Attention to What Matters
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913521787207680 |
|---|---|
| author | Silva, Pedro Luiz de Domenico, Antonio Maatouk, Ali Ayed, Fadhel |
| author_facet | Silva, Pedro Luiz de Domenico, Antonio Maatouk, Ali Ayed, Fadhel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19001 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pay Attention to What Matters Silva, Pedro Luiz de Domenico, Antonio Maatouk, Ali Ayed, Fadhel Computation and Language Artificial Intelligence 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. |
| title | Pay Attention to What Matters |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.19001 |