Empowering Small Language Models with Factual Hallucination-Aware Reasoning for Financial Classification

2026-01-04

arXiv.org

AI Summary

This study addresses the performance degradation of small language models in financial text classification due to factual hallucinations. It is the first to establish a positive correlation between factual hallucinations and misclassification in financial contexts. To mitigate this issue, the authors propose AAAI—a three-stage framework comprising Association Identification, Automated Detection, and Adaptive Inference. The framework first identifies potential hallucinations through association analysis, then employs an encoder-based factual verifier to automatically detect erroneous claims, and finally leverages a feedback-driven adaptive inference mechanism to guide the model toward corrected predictions. Experiments on three representative small language models demonstrate that the proposed approach effectively detects and alleviates hallucinations, leading to significant improvements in financial classification accuracy.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Small language models (SLMs) are increasingly used for financial classification due to their fast inference and local deployability. However, compared with large language models, SLMs are more prone to factual hallucinations in reasoning and exhibit weaker classification performance. This raises a natural question: Can mitigating factual hallucinations improve SLMs'financial classification? To address this, we propose a three-step pipeline named AAAI (Association Identification, Automated Detection, and Adaptive Inference). Experiments on three representative SLMs reveal that: (1) factual hallucinations are positively correlated with misclassifications; (2) encoder-based verifiers effectively detect factual hallucinations; and (3) incorporating feedback on factual errors enables SLMs'adaptive inference that enhances classification performance. We hope this pipeline contributes to trustworthy and effective applications of SLMs in finance.

Problem

• Small Language Models
• Factual Hallucination
• Financial Classification
• Misclassification
• Trustworthy AI

Innovation

• factual hallucination
• small language models
• financial classification
• adaptive inference
• hallucination detection