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arXiv Computation and Language By Renhao Pei, Yihong Liu, Sampo Pyysalo, Hinrich Sch\"utze, Shaoxiong Ji

Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?

Read the original on arXiv Computation and Language →

The paper explores whether structured linguistic reasoning traces can improve low‑resource machine translation by guiding large language models (LLMs). It proposes a pipeline that automatically generates step‑by‑step reasoning traces from Universal Dependencies treebanks, dictionaries, and grammar‑rule banks, and evaluates these traces in in‑context learning, supervised fine‑tuning, and reinforcement fine‑tuning on Xibe and Chintang. The results show that providing reliable reasoning traces at inference time significantly boosts translation quality, whereas using them as training data yields smaller, less consistent gains, indicating that LLMs can benefit from grammatical guidance but struggle to generate accurate analyses themselves.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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