Exploring the Reliability of Self-explanation and its Relationship with Classification in Language Model-driven Financial Analysis
This workshop paper investigates financial classification by language models, analyzing self-explanations’ factuality and causality to assess accuracy and trust.
What it examines
The study explores how language models perform financial classification using self-explanations in a zero-shot setting. It evaluates the link between explanation factuality, causality, and classification accuracy on public financial data, aiming to optimize both classification decisions and the reliability of the model’s reasoning.
What it concludes
Results reveal a strong link between explanation quality and classification accuracy in finance. Using factual self-explanations builds confidence in model decisions. Future studies should focus on larger models and additional tasks to extend these findings, enhancing trust and performance in financial applications.
Evidence objects
Researchers investigated language models ability for financial analysis by leveraging self-explanations that detail reasoning, revealing a surprising statistical link between factual and causal explanation quality and classification accuracy with impact.
key_findings bullet 1 · key_findings · validation V0
Authors introduced a novel confidence metric using factual and causal inconsistencies in self-explanations, validated through Chi-squared tests and German credit dataset experiments, thereby increasing trust in automated financial decisions remarkably.
key_findings bullet 2 · key_findings · validation V0
Comprehensive experiments using multiple language models and innovative data preparation methods showed that improved explanation accuracy correlates with better classification performance despite limited task variety and scale according to research.
key_findings bullet 3 · key_findings · validation V0
The study examines how LM-generated self-explanations correlate with financial analysis accuracy, impacting risk assessment and trading decisions. Building on explainable AI ideas, it applies zero-shot classification with language models in finance. Although derivative in approach, its focus on explanation quality and model trust renders its perspective compelling for further research.
key_findings bullet 4 · key_findings · validation V0
Raw abstract and provenance
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.
Source row: 772 · abstract type: unknown