跳到正文
arXiv cs.CL· Minghan Li, Guodong Zhou·· 3 天前AI 评分23

文言文能否作为中文 LLM 的高效推理语体?Wenyan Thinking 微调 Qwen3-4B-Thinking-2507

Does the Linguistic Register of Thinking Matter? Wenyan as a Compact Reasoning Register for Chinese LLMs

AI 导读

研究者提出 Wenyan Thinking,将中文推理模型的中间推理改写为文言文、仅保留简短直接答案,并在改写后的监督数据上微调 Qwen3-4B-Thinking-2507。在 MMLU-ProX-Lite-ZH、CEVAL 和 AGIEval-CN 的预算受限评测中,该方法显著缩短推理长度并降低缺答率;在主要基准上持续优于同等数据与配方训练的紧凑现代汉语对照组。

正文 · 原文

View PDF HTML (experimental)

Abstract:Visible reasoning traces of Chinese reasoning-oriented LLMs are typically written in modern vernacular Chinese, which may be verbose under limited decoding budgets. We ask whether the linguistic register of reasoning can be used to improve efficiency. We propose Wenyan Thinking, which emits intermediate reasoning in Classical Chinese (wenyanwen) while retaining a short direct-answer output. Starting from modern-Chinese reasoning traces, we rewrite only the reasoning field into wenyan and fine-tune Qwen3-4B-Thinking-2507 on the rewritten supervision. To distinguish register shift from generic compression, we construct a compact modern-Chinese control using the same source data, teacher family, base model, and SFT recipe. Across budget-sensitive evaluations on MMLU-ProX-Lite-ZH, CEVAL, and AGIEval-CN, Wenyan Thinking substantially reduces reasoning length and missing-answer rates under constrained decoding. Compact rewriting explains much of the efficiency gain, while wenyan consistently outperforms the compact modern-Chinese control on the primary benchmark and remains competitive across the additional benchmarks. These results identify linguistic register as a practical design axis for efficient visible reasoning in Chinese LLMs.
Comments: 19 pages
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2610.19741 [cs.CL]
  (or arXiv:2610.19741v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2610.19741

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Minghan Li [view email]
[v1] Wed, 14 Oct 2026 08:04:05 UTC (68 KB)

来源:arXiv cs.CL · arxiv.org