How to Enhance Causal Discrimination of Emotional Utterances: A Case on LLMs

Existing methods, including large language models (LLMs), excel at capturing semantic correlations between utterances, but often struggle to accurately distinguish specific causal relationships. This limitation poses a significant challenge for reasoning-intensive tasks in affective computing, where precise identification of emotional triggers and their effects is crucial. Our preliminary work demonstrated the potential of introducing i.i.d. noise terms within Structural Causal Models (SCMs) for the Emotion-Cause Pair Extraction (ECPE) task. However, this approach relied on end-to-end learning of high-dimensional latent representations, which hindered both scalability to LLMs and model interpretability. To address these issues, we conceptualize i.i.d. noise terms as token-level implicit causes—natural language expressions that reflect a speaker's underlying emotions, intentions, or situational context. Building on this insight, we introduce ICE (Implicit-Cause-Enhanced), an instruction-based framework that leverages implicit causes to enhance causal reasoning in LLMs. First, we design prompts that heuristically guide LLMs to generate implicit causes, which are then iteratively refined via an external evaluation mechanism. Second, by incorporating these implicit causes as intermediate reasoning steps, ICE improves the accuracy of emotion-cause pair prediction. Moreover, we distill the rationales produced by ICE into lightweight generative models, demonstrating that even small models can benefit from implicit-cause-driven reasoning. Extensive experiments in both instruction-based and distillation-based settings confirm the effectiveness, robustness, and interpretability of our approach.