On Emotion-Sensitive Decision Making of Small Language Model Agents
Small language models (SLM) are increasingly used as interactive decision-making agents, yet most decision-oriented evaluations ignore emotion as a causal factor influencing behavior. We study emotion-sensitive decision making by combining representation-level emotion induction with a structured game-theoretic evaluation. Emotional states are induced using activation steering derived from crowd-validated, real-world emotion-eliciting texts, enabling controlled and transferable interventions beyond prompt-based methods. We introduce a benchmark built around canonical decision templates that span cooperative and competitive incentives under both complete and incomplete information. These te...
| Authors | Jiaju Lin, Xingjian Du, Qingyun Wu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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