Explainable Text Classification with LLMs: Enhancing Performance through Dialectical Prompting and Explanation-Guided Training (2025.findings-emnlp)
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| Challenge: | Existing explanation methods that generate keywords may be less effective due to missing critical contextual information. |
| Approach: | They propose a new method to generate explanations for possible labels using LLMs and a dialectical prompt. |
| Outcome: | The proposed method significantly improves accuracy and explanation quality over state-of-the-art methods on multiple datasets from diverse domains. |
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Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, Zhicheng Dou
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| Challenge: | Large language models (LLMs) have been gaining in-depth performance in natural language processing domains. |
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Ling Shi, Xinwei Wu, Xiaohu Zhao, Hao Wang, Heng Liu, Yangyang Liu, Linlong Xu, Longyue Wang, Deyi Xiong, Weihua Luo
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| Challenge: | Recent studies show that pretraining and instruction-tuned LLMs can achieve impressive performance on a multitude of tasks. |
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