大语言模型在机器翻译中的推理：基于思维标记的合成数据生成

摘要

大型推理模型（LRMs）通过设计在回答查询前的自然语言思维过程，为问题解决开辟了新的可能性。尽管其在数学和编程任务中的能力广为人知，但它们在机器翻译（MT）任务上的影响仍未被充分探索。在本研究中，我们探讨了在不同资源水平和多种设置下，执行跨语言对机器翻译时生成中间标记的益处。我们发现，“思考标记”并未帮助LRMs更好地完成机器翻译任务。这一结果同样适用于那些经过微调、在翻译前进行推理的模型，这些模型采用了受人类翻译实践启发的蒸馏式思维链（CoT）方法。具体而言，使用详细说明如何逐步翻译的合成CoT解释对模型进行微调，并未超越标准的输入输出微调效果。然而，通过结合模块化翻译特定提示策略的输出构建中间标记，则带来了性能提升。我们的发现强调，在微调过程中，中间标记的贡献很大程度上取决于其中是否包含翻译尝试。更广泛地说，我们的结果表明，利用教师模型来精炼目标翻译或扩展平行语料库，比将它们的CoT解释蒸馏到“思考型”机器翻译模型中更具影响力。

English

Large reasoning models (LRMs) have led to new possibilities in terms of problem-solving, through the devising of a natural language thought process prior to answering a query. While their capabilities are well known across mathematics and coding tasks, their impact on the task of machine translation (MT) remains underexplored. In this work, we explore the benefits of the generation of intermediate tokens when performing MT across multiple language pairs of different levels of resourcedness and multiple setups. We find that "thinking tokens" do not help LRMs better perform MT. This result generalizes to models fine-tuned to reason before translating using distilled chain of thought (CoT) inspired by human translators' practices. Specifically, fine-tuning a model with synthetic CoT explanations detailing how to translate step-by-step does not outperform standard input-output fine-tuning. However, constructing the intermediate tokens by combining the outputs of modular translation-specific prompting strategies results in improvements. Our findings underscore that the contribution of intermediate tokens during fine-tuning highly depends on the presence of translation attempts within them. More broadly, our results suggest that using a teacher to refine target translations or to expand parallel corpora is more impactful than distilling their CoT explanations into "thinking" MT models.

大语言模型在机器翻译中的推理：基于思维标记的合成数据生成

LLM Reasoning for Machine Translation: Synthetic Data Generation over Thinking Tokens

摘要

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