Papers by Hansen Ha
Batch-ICL: Effective, Efficient, and Order-Agnostic In-Context Learning (2024.findings-acl)
Copied to clipboard
| Challenge: | In-context learning (ICL) is a meta-optimization process that affects performance . we develop a batch-based inference algorithm that is order-agnostic to ICL examples . |
| Approach: | They develop an order-agnostic inference algorithm that aggregates ICL examples in batches . they find it outperforms most permutations of ICL, and it even exceeds the best order . |
| Outcome: | The proposed method outperforms standard ICL examples while reducing computational resources. |