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.
By Renhao Pei, Yihong Liu, Sampo Pyysalo, Hinrich Sch\"utze, Shaoxiong Ji
arXiv:2607. 10386v1 Announce Type: cross Abstract: Large language models (LLMs) excel at generating long chains of thought, but long reasoning traces are often verbose and memory-inefficient.
By Zain Sarwar, Supriyo Chakraborty, Berkcan Kapusuzoglu, Chia-Hsuan Lee, Anirban Das, Stephen Rawls, Kartik Balasubramaniam, Sambit Sahu
arXiv:2505.16782v3 Announce Type: replace
Abstract: Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT...
By Xinghao Chen, Anhao Zhao, Heming Xia, Xuan Lu, Hanlin Wang, Yanjun Chen, Wei Zhang, Jian Wang, Wenjie Li, Xiaoyu Shen
arXiv:2510. 08647v2 Announce Type: replace-cross Abstract: Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during inference for performance.
By Chengzhengxu Li, Xiaoming Liu, Zhaohan Zhang, Shengchao Liu, Guoxin Ma, Yu Lan, Cong Wang, Chao Shen
arXiv:2607. 29287v1 Announce Type: cross Abstract: Multi-domain machine translation (MDMT) poses a unique challenge due to varying levels of linguistic complexity across domains.
By Yongshi Ye, Biao Fu, Chongxuan Huang, Yidong Chen, Xiaodong Shi
Translation cascades for reasoning translate the query from another language to English, reason in English, and translate the answer back to the original language. This is a competitive approach to multilingual reasoning, but structurally lossy, since each stage discards information later stages may need, including cues for cultural grounding, register, and disambiguation.