让大模型在金融法律领域推理更可信，自动补全思考过程并优化答案路径。
Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains
- 通过领域特定思维链数据微调模型，激活专业推理步骤。
- 在股票推荐和法律问答任务中准确率提升12.3%与15.6%，解释性显著增强。
- 适合需要高可信度解释的金融、法律等高风险决策场景使用。
大型语言模型广泛应用于下游领域，但在金融投资、法律问答等高风险任务中，现有模型通常仅生成简短回答而缺乏推理过程与解释，降低用户决策信心。尽管原始思维链（CoT）具有潜力，但其推理过程缺乏自我修正机制。本文提出Domaino1s，通过监督微调与树搜索增强模型在领域任务中的推理能力。构建了CoT-stock-2k与CoT-legal-2k两个数据集，用于训练模型根据判断激活领域特异性推理步骤。提出选择性树探索策略，自发探索解空间并采样最优推理路径以提升性能。引入PROOF-Score新评估指标，从多维度衡量模型解释性，补充传统准确率。在股票投资推荐与法律推理问答任务上的大量实验表明，Domaino1s在性能与可解释性方面均表现领先。代码已开源：https://github.com/Hyalinesky/Domaino1s。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are widely applied to downstream domains. However, current LLMs for high-stakes domain tasks, such as financial investment and legal QA, typically generate brief answers without reasoning processes and explanations. This limits users' confidence in making decisions based on their responses. While original CoT shows promise, it lacks self-correction mechanisms during reasoning. This work introduces Domain$o1$s, which enhances LLMs' reasoning capabilities on domain tasks through supervised fine-tuning and tree search. We construct CoT-stock-2k and CoT-legal-2k datasets for fine-tuning models that activate domain-specific reasoning steps based on their judgment. Additionally, we propose Selective Tree Exploration to spontaneously explore solution spaces and sample optimal reasoning paths to improve performance. We also introduce PROOF-Score, a new metric for evaluating domain models' explainability, complementing traditional accuracy metrics with richer assessment dimensions. Extensive experiments on stock investment recommendation and legal reasoning QA tasks demonstrate Domaino1s's leading performance and explainability. Our code is available at https://github.com/Hyalinesky/Domaino1s.
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