Exploring the Reliability of Self-explanation and its Relationship with Classification in Language Model-driven Financial Analysis
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Abstract (arXiv)
Language models (LMs) have exhibited exceptional versatility in reasoning and in-depth financial analysis through their proprietary information processing capabilities. Previous research focused on evaluating classification performance while often overlooking explainability or pre-conceived that refined explanation corresponds to higher classification accuracy. Using a public dataset in finance domain, we quantitatively evaluated self-explanations by LMs, focusing on their factuality and causality. We identified the statistically significant relationship between the accuracy of classifications and the factuality or causality of self-explanations. Our study built an empirical foundation for approximating classification confidence through self-explanations and for optimizing classification via proprietary reasoning.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 20 of its 30 references that are in the map. First posted on arXiv 20 Mar 2025.
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