@inproceedings{du-etal-2025-explainable,
    title = "Explainable Text Classification with {LLM}s: Enhancing Performance through Dialectical Prompting and Explanation-Guided Training",
    author = "Du, Huaming  and
      Yuan, Lei  and
      Feng, Cancan  and
      Liu, Guisong  and
      Kou, Gang  and
      Yang, Carl",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.685/",
    doi = "10.18653/v1/2025.findings-emnlp.685",
    pages = "12800--12816",
    ISBN = "979-8-89176-335-7",
    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."
}