Integration of Explainable AI Techniques with Large Language Models for Enhanced Interpretability for Sentiment Analysis

• URL: http://arxiv.org/abs/2503.11948v1
• Date: Sat, 15 Mar 2025 01:37:54 GMT
• Title: Integration of Explainable AI Techniques with Large Language Models for Enhanced Interpretability for Sentiment Analysis
• Authors: Thivya Thogesan, Anupiya Nugaliyadde, Kok Wai Wong,
• Abstract summary: Interpretability remains a key difficulty in sentiment analysis with Large Language Models (LLMs)<n>This research introduces a technique that applies SHAP (Shapley Additive Explanations) by breaking down LLMs into components such as embedding layer,encoder,decoder and attention layer.<n>The method is evaluated using the Stanford Sentiment Treebank (SST-2) dataset, which shows how different sentences affect different layers.
• Score: 0.5120567378386615
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interpretability remains a key difficulty in sentiment analysis with Large Language Models (LLMs), particularly in high-stakes applications where it is crucial to comprehend the rationale behind forecasts. This research addressed this by introducing a technique that applies SHAP (Shapley Additive Explanations) by breaking down LLMs into components such as embedding layer,encoder,decoder and attention layer to provide a layer-by-layer knowledge of sentiment prediction. The approach offers a clearer overview of how model interpret and categorise sentiment by breaking down LLMs into these parts. The method is evaluated using the Stanford Sentiment Treebank (SST-2) dataset, which shows how different sentences affect different layers. The effectiveness of layer-wise SHAP analysis in clarifying sentiment-specific token attributions is demonstrated by experimental evaluations, which provide a notable enhancement over current whole-model explainability techniques. These results highlight how the suggested approach could improve the reliability and transparency of LLM-based sentiment analysis in crucial applications.

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