库帕思高质量金融思维链（Chain-of-Thought）数据集

本数据集面向教育大模型研发团队、金融科技企业及金融机构，旨在解决金融领域专业知识体系复杂、实务推理能力不足等痛点。通过系统化清洗和思维链重构，整合了银行业务、证券投资、保险精算、基金理财等金融细分领域的试题资源，涵盖从基础理论到实务应用的全方位内容。

通过思维链训练，可为基座大模型提供金融领域的通识能力培养，同时助力金融垂类模型在复杂业务场景下的专业能力提升。与传统多源题库不同，本数据集对跨领域试题进行了统一的思维链格式标准化，确保模型在银行、证券、保险等不同子领域间迁移学习时保持推理逻辑的一致性。

This dataset is targeted at educational large language model (LLM) R&D teams, fintech enterprises and financial institutions, aiming to address pain points including the complexity of professional knowledge systems in the financial sector and the deficiency in practical reasoning capabilities. Through systematic data cleaning and chain-of-thought (CoT) restructuring, it integrates question resources from segmented financial fields such as banking business, securities investment, insurance actuarial science, and fund and wealth management, covering comprehensive content ranging from basic theories to practical applications.

Through chain-of-thought training, this dataset can help cultivate general financial literacy capabilities for base LLMs, while assisting financial domain-specific LLMs to improve their professional capabilities in complex business scenarios. Unlike traditional multi-source question banks, this dataset has uniformly standardized the chain-of-thought format for cross-domain questions, ensuring that models maintain consistent reasoning logic when conducting transfer learning across different sub-sectors such as banking, securities, and insurance.

搜集汇总

背景与挑战

背景概述

该数据集面向教育大模型研发团队、金融科技企业及金融机构，旨在解决金融领域专业知识复杂和实务推理能力不足的痛点。它通过系统化清洗和思维链重构，整合了金融细分领域的试题资源，涵盖从基础理论到实务应用的内容，并采用统一的思维链格式标准化，以支持模型在跨子领域迁移学习时保持推理逻辑的一致性。