Improving Minimum Bayes Risk Decoding with Multi-Prompt.
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| Titel: | Improving Minimum Bayes Risk Decoding with Multi-Prompt. |
|---|---|
| Autoren: | Heineman D; School of Interactive Computing, Georgia Institute of Technology., Dou Y; School of Interactive Computing, Georgia Institute of Technology., Xu W; School of Interactive Computing, Georgia Institute of Technology. |
| Quelle: | Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing [Proc Conf Empir Methods Nat Lang Process] 2024 Nov; Vol. 2024, pp. 22525-22545. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: ACL Country of Publication: United States NLM ID: 101669294 Publication Model: Print Cited Medium: Print NLM ISO Abbreviation: Proc Conf Empir Methods Nat Lang Process Subsets: PubMed not MEDLINE |
| Imprint Name(s): | Publication: 1997- : Somerset, N.J. : ACL Original Publication: Philadelphia, Pa. : University of Pennsylvania, 1996- |
| Abstract: | While instruction fine-tuned LLMs are effective text generators, sensitivity to prompt construction makes performance unstable and sub-optimal in practice. Relying on a single 'best' prompt cannot capture all differing approaches to a generation problem. Using this observation, we propose multi-prompt decoding, where many candidate generations are decoded from a prompt bank at inference-time. To ensemble candidates, we use Minimum Bayes Risk (MBR) decoding, which selects a final output using a trained value metric. We show multi-prompt improves MBR across a comprehensive set of conditional generation tasks (Figure 1), and show this is a result of estimating a more diverse and higher quality candidate space than that of a single prompt. Further experiments confirm multi-prompt improves generation across tasks, models and metrics. |
| Grant Information: | R01 LM014600 United States LM NLM NIH HHS |
| Entry Date(s): | Date Created: 20250704 Latest Revision: 20250705 |
| Update Code: | 20250705 |
| PubMed Central ID: | PMC12226151 |
| DOI: | 10.18653/v1/2024.emnlp-main.1255 |
| PMID: | 40612446 |
| Datenbank: | MEDLINE |
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