MICA combines incremental per-turn distance rewards and Monte Carlo returns from a shared potential function over user support states to create a mixed advantage signal that enables stable multi-turn RL optimization for emotional support dialogues.
Liu, Jinfeng Zhou, Alvionna S
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Authors build an emotional intensity dataset and fine-tune generative LLMs to predict continuous 0-100 scores, claiming outperformance over classification baselines plus generalization to sentiment and arousal.
A holistic survey of affective computing for intelligent agents covering emotion understanding via multimodal data, affective cognition, emotional expression synthesis, key challenges, and future directions emphasizing generative technologies.
citing papers explorer
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MICA: Multi-granularity Intertemporal Credit Assignment for Long-Horizon Emotional Support Dialogue
MICA combines incremental per-turn distance rewards and Monte Carlo returns from a shared potential function over user support states to create a mixed advantage signal that enables stable multi-turn RL optimization for emotional support dialogues.
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Beyond Sentiment Classification: A Generative Framework for Emotion Intensity Evaluation in Text
Authors build an emotional intensity dataset and fine-tune generative LLMs to predict continuous 0-100 scores, claiming outperformance over classification baselines plus generalization to sentiment and arousal.
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Intelligent Agents with Emotional Intelligence: Current Trends, Challenges, and Future Prospects
A holistic survey of affective computing for intelligent agents covering emotion understanding via multimodal data, affective cognition, emotional expression synthesis, key challenges, and future directions emphasizing generative technologies.
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