Abstract
Zero-shot In-context learning is the phenomenon where models can perform a task given only the instructions. However, pre-trained large language models are known to be poorly calibrated for zero-shot tasks. One of the most effective approaches to handling this bias is to adopt a contrastive decoding objective, which accounts for the prior probability of generating the next token by conditioning on a context. This work introduces an Anti-Language Model objective with a decay factor designed to address the weaknesses of In-context Machine Translation. We conduct our experiments across 3 model types and sizes, 3 language directions, and for both greedy decoding and beam search. The proposed method outperforms other state-of-the-art decoding objectives, with up to 20 BLEU point improvement from the default objective in some settings.- Anthology ID:
- 2024.findings-naacl.216
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3403–3420
- Language:
- URL:
- https://aclanthology.org/2024.findings-naacl.216
- DOI:
- Cite (ACL):
- Suzanna Sia, Alexandra DeLucia, and Kevin Duh. 2024. Anti-LM Decoding for Zero-shot In-context Machine Translation. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3403–3420, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- Anti-LM Decoding for Zero-shot In-context Machine Translation (Sia et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2024.findings-naacl.216.pdf