arXiv cs.CL· Jiashu Yang, Ningning Wang, Yian Zhao, Chaoran Feng, Junjia Du, Hao Pang, Miao Yu, Xuxin Cheng·· 13 小时前AI 评分24
Kongzi：面向历史理解与推理的事实增强大语言模型
Kongzi: A Historical Large Language Model with Fact Enhancement
AI 导读
研究者提出历史领域语言模型 Kongzi，结合历史语料继续预训练、监督微调和事实增强强化学习，其事实性奖励通过知识图谱相似度比对生成回答与史料中的实体和关系。在 0.5B 与 7B 两个规模上评测历史问答与反事实推理，Kongzi-7B 在三位 LLM 评委给出的历史准确性评分中均为对比模型最高。
正文 · 原文
Abstract:Large Language Models (LLMs) can exhibit factual inaccuracies during long-chain reasoning or under misleading prompts, limiting their reliability in historical analysis. We propose Kongzi, a language model for historical understanding and reasoning that combines continued pre-training on historical corpora, supervised fine-tuning, and fact-enhanced reinforcement learning. Its factuality reward compares entities and relations extracted from generated responses with historical references through knowledge-graph similarity. We evaluate 0.5B and 7B models on historical question answering and counterfactual reasoning, using reported ratings from three LLM judges, and on CMMLU-Chinese-History. Kongzi-7B receives the highest historical-accuracy ratings among the compared models under all three judges. Reinforcement learning improves the mean answer scores of both model sizes and their CMMLU-Chinese-History accuracy, although Kongzi-7B remains below the strongest reported baselines on that benchmark. These descriptive results support further study of factuality rewards for historical language models.
| Comments: | 22 pages, 14 figures, 4 tables. Revised training and evaluation descriptions; corrected derived evaluation summaries and figures; updated author list |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2504.09488 [cs.CL] |
| (or arXiv:2504.09488v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2504.09488 arXiv-issued DOI via DataCite |
Submission history
From: Jiashu Yang [view email]
[v1]
Sun, 13 Apr 2025 09:01:05 UTC (27,257 KB)
[v2]
Thu, 22 Oct 2026 01:19:25 UTC (26,013 KB)
来源：arXiv cs.CL · arxiv.org