Agentar-DeepFinance-100K: A Large-Scale Financial Dataset via Systematic Chain-of-Thought Synthesis Optimization

TL;DR

This paper introduces Agentar-DeepFinance-100K, a large-scale financial reasoning dataset created through a systematic chain-of-thought synthesis process, improving financial reasoning capabilities of language models.

Contribution

It presents a novel CoT synthesis pipeline with Multi-perspective Knowledge Extraction and Self-Corrective Rewriting, along with an analysis framework called CoT Cube for effective financial reasoning data construction.

Findings

Models trained on the dataset outperform existing benchmarks.

The systematic synthesis approach enhances reasoning depth and accuracy.

Insights from CoT Cube guide better knowledge space construction.

Abstract

Recent advancements in large language models (LLMs) have demonstrated remarkable general reasoning capabilities, holding significant potential for applications in the financial domain, a field that requires robust and reliable reasoning. It has been demonstrated that distilling high-quality chain-of-thought (CoT) rationales from advanced general reasoning models offers a promising and efficient path to the financial reasoning model. However, existing CoT synthesis methods suffer from shallow CoT sampling, leaving the question of how to construct a well-designed knowledge space for finance reasoning unexplored. In this paper, we present Agentar-DeepFinance-100K, a large-scale financial reasoning dataset characterized by its systematic CoT synthesis optimization. We first introduce a comprehensive CoT synthesis pipeline featuring Multi-perspective Knowledge Extraction (MKE) and Self-Corrective Rewriting (SCR) to generate exhaustive and deep financial reasoning trajectories. Furthermore, a systematic investigation, termed CoT Cube, is conducted to analyze critical factors that influence CoT effectiveness, such as necessity, length and synthesizer, yielding valuable insights for high-quality financial CoT construction. Experiments demonstrate that models trained on our Agentar-DeepFinance-100K achieve significant improvements on financial benchmarks. We publicly release Agentar-DeepFinance-100K , hoping to advance the research in financial reasoning models.