Attention Alignment and Flexible Positional Embeddings Improve Transformer Length Extrapolation (2024.findings-naacl)
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| Challenge: | Existing methods for length extrapolation are tailored for natural language modeling, a task known to have strong recency bias. |
| Approach: | They propose two attention alignment strategies to improve T5's long-context utilization capability without fine-tuning. |
| Outcome: | The proposed methods improve the long-context utilization capability of T5 on language modeling, retrieval, multi-document question answering, and code completion tasks without any fine-tuning. |
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