Researchers have developed MINT, a reinforcement learning framework designed to enhance discourse move diversity in multi-turn empathic dialogues between large language models (LLMs). This innovation addresses LLMs' tendency to reuse similar tactics repeatedly, which is less common in human interactions and can limit the effectiveness of emotional support. By improving tactic novelty, MINT boosts overall empathy quality by 25.3% while reducing repetition by 26.3%, offering a significant step forward for more natural and supportive AI conversations.
Read the full article at arXiv cs.CL (NLP)
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Ali NematiWritten by Ali
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