FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning
This paper presents FinCoT, a structured chain-of-thought (CoT) prompting
approach that incorporates insights from domain-specific expert financial
reasoning to guide the reasoning traces of large language models. We
investigate that there are three main prompting styles in FinNLP: (1) standard
prompting--zero-shot prompting; (2) unstructured CoT--CoT prompting without an
explicit reasoning structure, such as the use of tags; and (3) structured CoT
prompting--CoT prompting with explicit instructions or examples that define
structured reasoning steps. Previously, FinNLP has primarily focused on prompt
engineering with either standard or unstructured CoT prompting. However,
structured CoT prompting has received limited attention in prior work.
Furthermore, the design of reasoning structures in structured CoT prompting is
often based on heuristics from non-domain experts. In this study, we
investigate each prompting approach in FinNLP. We evaluate the three main
prompting styles and FinCoT on CFA-style questions spanning ten financial
domains. We observe that FinCoT improves performance from 63.2% to 80.5% and
Qwen-2.5-7B-Instruct from 69.7% to 74.2%, while reducing generated tokens
eight-fold compared to structured CoT prompting. Our findings show that
domain-aligned structured prompts not only improve performance and reduce
inference costs but also yield more interpretable and expert-aligned reasoning
traces.
6859359b0e4ad7e21975834b
Natapong Nitarach
claimed_verified
2025-06-24T08:09:20.191Z
64705d3890482b0e0f6591ed
https://cdn-avatars.huggingface.co/v1/production/uploads/64705d3890482b0e0f6591ed/HqOaaRjzkXrC8POGtZYwh.jpeg
Natapong Nitarach (Schwyter)
user
natnitaract
6859359b0e4ad7e21975834c
Warit Sirichotedumrong
6859359b0e4ad7e21975834d
Panop Pitchayarthorn
6859359b0e4ad7e21975834e
Pittawat Taveekitworachai
claimed_verified
2025-06-24T08:14:47.952Z
615313b0793ef66b3324da1f
https://cdn-avatars.huggingface.co/v1/production/uploads/615313b0793ef66b3324da1f/VyJniD3dxbV5a2CMgVVQ2.jpeg
Pittawat Taveekitworachai
user
pittawat
6859359b0e4ad7e21975834f
Potsawee Manakul
6859359b0e4ad7e219758350
Kunat Pipatanakul
claimed_verified
2025-07-15T19:11:48.647Z
62d192c2d50433c35eb1b48e
https://cdn-avatars.huggingface.co/v1/production/uploads/62d192c2d50433c35eb1b48e/VjmDu8GOIuLuQNBQdQLLS.png
Kunat Pipatanakul
user
kunato
FinCoT: Grounding Chain-of-Thought in Expert Financial Reasoning
This paper presents FinCoT, a structured chain-of-thought (CoT) prompting
approach that incorporates insights from domain-specific expert financial
reasoning to guide the reasoning traces of large language models. We
investigate that there are three main prompting styles in FinNLP: (1) standard
prompting--zero-shot prompting; (2) unstructured CoT--CoT prompting without an
explicit reasoning structure, such as the use of tags; and (3) structured CoT
prompting--CoT prompting with explicit instructions or examples that define
structured reasoning steps. Previously, FinNLP has primarily focused on prompt
engineering with either standard or unstructured CoT prompting. However,
structured CoT prompting has received limited attention in prior work.
Furthermore, the design of reasoning structures in structured CoT prompting is
often based on heuristics from non-domain experts. In this study, we
investigate each prompting approach in FinNLP. We evaluate the three main
prompting styles and FinCoT on CFA-style questions spanning ten financial
domains. We observe that FinCoT improves performance from 63.2% to 80.5% and
Qwen-2.5-7B-Instruct from 69.7% to 74.2%, while reducing generated tokens
eight-fold compared to structured CoT prompting. Our findings show that
domain-aligned structured prompts not only improve performance and reduce
inference costs but also yield more interpretable and expert-aligned reasoning
traces.
A structured chain-of-thought prompting method in financial natural language processing improves performance and reduces computational cost while enhancing interpretability.
chain-of-thought FinCoT standard prompting unstructured CoT structured CoT prompt engineering CFA-style questions domain-aligned inference costs