Peeking Inside LLMs: Leveraging Internal Artifacts of LLMs for Enhancing Reliability in Legal Classification

2026-06-18

AI Summary

This work addresses the reliability challenges posed by errors and hallucinations in large language models (LLMs) when applied to high-stakes legal classification tasks. To mitigate this issue, the study proposes a lightweight binary verifier that dynamically assesses the correctness of model predictions by leveraging internal model artifacts—specifically activation values and attention weights—as trustworthiness signals. This approach marks the first systematic use of such internal representations for calibration in legal AI contexts. Evaluated on two critical legal tasks, namely bail decision prediction and statutory violation classification, the verifier significantly improves the detection of erroneous predictions, thereby enhancing the overall reliability of LLM-based legal reasoning systems without requiring extensive retraining or architectural modifications.

Abstract

Large Language Models (LLMs) are increasingly being adopted in the legal domain. However, despite their strong performance, LLMs are prone to generating incorrect or hallucinated outputs, raising serious concerns about their reliability in high-stakes domains such as law. Detecting the correctness of responses of LLM-based systems is therefore a critical challenge. In this work, we explore the potential of leveraging internal artifacts of LLM to detect the correctness of their predictions in legal-domain classification tasks. We develop approaches that utilize features derived from these internal artifacts to build downstream classifiers capable of identifying incorrect LLM outputs. We evaluate our approach on two representative legal classification tasks: bail decision prediction and statute violation prediction. Our experimental results demonstrate that LLMs' internal artifacts are reliable indicators for detecting incorrect predictions in legal classification tasks, and can be applied to enhance the reliability of LLM-based classification systems.

Problem

• Large Language Models
• Reliability
• Legal Classification
• Hallucination
• Correctness Detection

Innovation

• internal artifacts
• reliability enhancement
• legal classification
• hallucination detection
• LLM interpretability