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        <title>Anti-LM Decoding for Zero-shot In-context Machine Translation</title>
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            <namePart type="given">Helena</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Anti-LM Decoding for Zero-shot In-context Machine Translation
%A Sia, Suzanna
%A DeLucia, Alexandra
%A Duh, Kevin
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F sia-etal-2024-anti
%X 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.
%R 10.18653/v1/2024.findings-naacl.216
%U https://aclanthology.org/2024.findings-naacl.216
%U https://doi.org/10.18653/v1/2024.findings-naacl.216
%P 3403-3420

Markdown (Informal)

[Anti-LM Decoding for Zero-shot In-context Machine Translation](https://aclanthology.org/2024.findings-naacl.216) (Sia et al., Findings 2024)

• Anti-LM Decoding for Zero-shot In-context Machine Translation (Sia et al., Findings 2024)

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.