Semantic and Structural Analysis of Implicit Biases in Large Language Models: An Interpretable Approach

TL;DR

This paper introduces an interpretable bias detection method for large language models that identifies hidden social biases through semantic and structural analysis, enhancing transparency and reliability.

Contribution

It presents a novel bias detection approach combining nested semantic representation with contextual contrast, improving interpretability and detection accuracy in LLMs.

Findings

Achieves high bias detection accuracy across multiple stereotype dimensions

Maintains semantic consistency and output stability during bias analysis

Provides transparent insights into internal bias mechanisms of language models

Abstract

This paper addresses the issue of implicit stereotypes that may arise during the generation process of large language models. It proposes an interpretable bias detection method aimed at identifying hidden social biases in model outputs, especially those semantic tendencies that are not easily captured through explicit linguistic features. The method combines nested semantic representation with a contextual contrast mechanism. It extracts latent bias features from the vector space structure of model outputs. Using attention weight perturbation, it analyzes the model's sensitivity to specific social attribute terms, thereby revealing the semantic pathways through which bias is formed. To validate the effectiveness of the method, this study uses the StereoSet dataset, which covers multiple stereotype dimensions including gender, profession, religion, and race. The evaluation focuses on several key metrics, such as bias detection accuracy, semantic consistency, and contextual sensitivity. Experimental results show that the proposed method achieves strong detection performance across various dimensions. It can accurately identify bias differences between semantically similar texts while maintaining high semantic alignment and output stability. The method also demonstrates high interpretability in its structural design. It helps uncover the internal bias association mechanisms within language models. This provides a more transparent and reliable technical foundation for bias detection. The approach is suitable for real-world applications where high trustworthiness of generated content is required.