Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains
This addresses the need for explainable AI in high-stakes decision-making domains, though it is incremental as it builds on existing CoT methods.
The paper tackles the problem of LLMs generating brief, unexplained answers in high-stakes domains like finance and law by introducing Domaino1s, which enhances reasoning through fine-tuning and tree search, achieving leading performance and explainability in stock investment and legal QA tasks.
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.