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Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation
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Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation
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Beam search is widely used in neural machine translation, and usually improves translation quality compared to greedy search. It has been widely observed that, however, beam sizes larger than 5 hurt translation quality. We explain why this happens, and propose several methods to address this problem. Furthermore, we discuss the optimal stopping criteria for these methods. Results show that our hyperparameter-free methods outperform the widely-used hyperparameter-free heuristic of length normalization by +2.0 BLEU, and achieve the best results among all methods on Chinese-to-English translation.
Forward citations
Cited by 2 Pith papers
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LBR: Towards Mitigating Length Bias in Large Language Models for Recommendation
LBR removes length bias in LLM recommenders via length-aware attention offsets and Trie-branching information-length normalization, improving accuracy and fairness with negligible cost.
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MASPRM: Multi-Agent System Process Reward Model
MASPRM trains a per-agent, per-message value head from outcome-only MCTS rollouts and uses it to guide step-level beam search and MCTS, improving exact match on GSM8K by up to +30.7 points over a greedy multi-agent pass.
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