用行为金融学构建理财大模型训练数据，8B模型性能媲美32B且成本降80%。
Synthesizing Behaviorally-Grounded Reasoning Chains: A Data-Generation Framework for Personal Finance LLMs
- 融合行为金融学与财务背景，生成可复现的推理数据集
- 19k样本微调后，8B模型在准确性、个性化上媲美14-32B大模型
- 适合需要低成本高精度理财AI的开发者与金融机构
个性化财务建议需考虑用户目标、约束、风险偏好及司法辖区。以往大模型研究多聚焦于投资者支持系统；近年虽有大量研究通过智能体流水线处理预算、债务管理、退休规划与遗产规划等任务，但维护成本高，实际收益不足预期的25%。本文提出一种新颖且可复现的框架，将财务背景与行为金融学研究结合，构建端到端理财顾问的监督数据。基于该框架，我们生成了19,000条推理样本，并对Qwen-3-8B模型进行全面微调。通过保留测试集和盲评大模型评审，结果表明，经精心数据构建与行为整合后，我们的8B模型在事实准确性、流畅性与个性化指标上达到与14-32B参数模型相当的性能，同时相比后者降低80%成本。
原文摘要 · Abstract (English)
Personalized financial advice requires consideration of user goals, constraints, risk tolerance, and jurisdiction. Prior LLM work has focused on support systems for investors and financial planners. Simultaneously, numerous recent studies examine broader personal finance tasks, including budgeting, debt management, retirement, and estate planning, through agentic pipelines that incur high maintenance costs, yielding less than 25% of their expected financial returns. In this study, we introduce a novel and reproducible framework that integrates relevant financial context with behavioral finance studies to construct supervision data for end-to-end advisors. Using this framework, we create a 19k sample reasoning dataset and conduct a comprehensive fine-tuning of the Qwen-3-8B model on the dataset. Through a held-out test split and a blind LLM-jury study, we demonstrate that through careful data curation and behavioral integration, our 8B model achieves performance comparable to significantly larger baselines (14-32B parameters) across factual accuracy, fluency, and personalization metrics while incurring 80% lower costs than the larger counterparts.
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