Title: LLM Reasoning for Machine Translation: Synthetic Data Generation over Thinking Tokens
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Large Language Models (LLMs), Machine Translation (MT), Chain-of-Thought (CoT), Thinking Models, Fine-tuning, Distillation, Prompting strategies

Abstract: 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 CoT based on MT prompting strategies results in improvements. Our findings underscore that the contribution of a CoT during fine-tuning highly depends on the presence of translation attempts in 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.

TL;DR: Using a teacher model to augment training data or refine translation targets better helps MT performance during fine-tuning that teacher-distilled CoT.