Explainable Text Classification with LLM s: Enhancing Performance through Dialectical Prompting and Explanation-Guided Training (2025)

Abstract: Large Language Models (LLMs) have achieved impressive success across a range of natural language processing tasks. However, they still underperform in text classification tasks compared to fine-tuned small models. This can be linked to complexities in addressing context-dependent expressions and complex linguistic phenomena. In contrast, fine-tuned small models typically achieve high prediction accuracy but often lack explanations for predictions. Existing explanation methods that generate keywords may be less effective due to missing critical contextual information. To mitigate these challenges, we propose a novel method termed Dialectical Explanation Training (**DET**). This method introduces a new prompting strategy, Dialectical Prompting, and integrates it with Explanation-Guided Training. Dialectical Prompting uses LLMs with our designed dialectical prompt to generate explanations for possible labels. These explanations handle context-dependent expressions and complex linguistic phenomena by considering multiple perspectives and providing rich, contextually relevant information. Explanation-Guided Training employs these explanations as features for training a small model, which combines the advantages of dialectical explanations and the predictive power of fine-tuned models to improve overall accuracy and interpretability. In addition, we incorporate the theory of Evidential Deep Learning, which further enhances the model’s classification performance and quantify the uncertainty of its predictions. Extensive experiments on multiple datasets from diverse domains have demonstrated that our proposed model significantly improves accuracy and explanation quality over state-of the-art methods in text classification.

## introduction text classification is a foundational task underpinning many applications, where both interpretability * corresponding author and accuracy are crucial, especially in high-stakes domains like finance, law, and value alignment @xcite . large language models (llms), with their extensive pretraining on diverse datasets, have demonstrated remarkable success @xcite . however, their performance in text classification significantly lags behind that of small models such as bert and deberta @xcite , which, despite their high prediction accuracy, often fail to provide explanations for their predictions. the dual challenge of optimizing both performance and interpretability in text classification drives our research. to tackle this, we propose a method that integrates dialectical prompting with explanation-guided training, aiming to enhance both performance and interpretability. previous studies, such as those by @xcite , have shown that llms struggle with context-dependent expressions and complex linguistic phenomena due to their reliance on statistical patterns rather than deep semantic understanding @xcite . additionally, llms are often overly confident and prone to hallucinations. we aim to resolve these challenges via using dialectical explanations to strengthen the model's semantic understanding, thereby improving its ability to deal with linguistic subtleties. figure 1 highlights a case of complex linguistic phenomena, where the sentence contains multiple layers of meaning. the conventional llm misclassifies by favoring one interpretation, while the dialectical approach considers both, achieving correct classification with lower uncertainty foot_0 . an example of context-dependent expressions is the sentence "you have a strong personality." in american cultural context, this phrase is generally viewed positively, implying confidence and assertiveness. ## methodology the framework det we proposed, as shown in figure 2 , mainly includes dialectical prompting and explanation-guided training to enhance both text classification and explanation performance. ## conclusion in this paper, we propose a novel dialectical explanation training method that combines dialectical prompting, explanation-guided training, and edl theory to enhance both the performance and explainability of text classification. by generating dialectical explanations and incorporating them directly into the training process, our method effectively tackles challenges such as context-dependent expressions, complex linguistic phenomena, and poor interpretability. extensive experiments demonstrate that our method achieves significant improvements across multiple metrics. these results underscore the potential of our approach to make text classification more reliable and interpretable, particularly in high-stakes domains such as finance, law, and value alignment, where transparency and robustness are essential.