LLM Reasoning for Machine Translation: Synthetic Data Generation over
  Thinking Tokens
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.
68eefecd486b78128f0e3388
Armel Zebaze
68eefecd486b78128f0e3389
Rachel Bawden
68eefecd486b78128f0e338a
Benoît Sagot
LLM Reasoning for Machine Translation: Synthetic Data Generation over
  Thinking Tokens
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.