Emotion-o1：大语言模型中情感理解的自适应长推理
Emotion-o1: Adaptive Long Reasoning for Emotion Understanding in LLMs
AI总结：
Emotion-o1提出自适应思维链框架，根据情感任务复杂度动态调整推理长度，通过蒸馏、微调和强化学习训练，在四个情感任务上显著提升F1分数，并大幅减少推理长度。
摘要：
长思维链（CoT）推理在增强大语言模型（LLMs）的情感理解性能方面显示出巨大潜力。然而，当前固定长度的CoT方法难以在推理深度和效率之间取得平衡。简单任务（如情感分类）被过度推理，而复杂任务（如讽刺理解）则缺乏深度。为填补这一空白，我们提出了Emotion-o1，一个自适应CoT框架，它根据情感任务的复杂性动态调整推理长度。Emotion-o1通过从面向推理的LLM中蒸馏自适应CoT模式进行训练，随后进行监督微调和强化学习，其奖励函数包含四个部分，分别针对准确性、简洁性、结构性和冗余性。在四个情感任务上的实验结果表明：（1）Emotion-o1相较于其基础模型有显著提升，F1分数分别提高了10%（情感）、5%（情绪）、18%（幽默）和27%（讽刺）。（2）在情感和讽刺任务中，我们的8B模型展现出优于先进LLM的性能，分别超过Grok-3 1.1%和Claude-3.7 2%。（3）该框架在保持准确性的同时，与OpenAI-o1相比将推理长度减少了83%，展示了有效的精度-效率优化。Emotion-o1有效地平衡了LLMs中情感理解的推理深度和效率。
英文摘要：
Long chain-of-thought (CoT) reasoning has shown great promise in enhancing the emotion understanding performance of large language models (LLMs). However, current fixed-length CoT methods struggle to balance reasoning depth and efficiency. Simple tasks (e.g., sentiment classification) are over-reasoned, while complex tasks (e.g., sarcasm understanding) lack depth. To fill this gap, we present Emotion-o1, an adaptive CoT framework that dynamically adjusts reasoning length based on emotion-task complexity. Emotion-o1 is trained by distilling adaptive CoT patterns from a reasoning-oriented LLM, followed by supervised fine-tuning and reinforcement learning with a four-part reward targeting accuracy, brevity, structure, and redundancy. Experimental results on four emotion tasks highlight: (1) Emotion-o1 demonstrates significant improvements over its backbone, with F1 score increases of 10%(Sentiment), 5%(Emotion), 18%(Humor), and 27%(Sarcasm). (2) In sentiment and sarcasm tasks, our 8B model demonstrates superior performance against advanced LLMs, outperforming Grok-3 by 1.1% and Claude-3.7 by 2%. (3) The framework maintains accuracy while reducing reasoning length by 83% compared to OpenAI-o1, demonstrating effective precision-efficiency optimization. Emotion-o1 effectively balances reasoning depth and efficiency for emotion understanding in LLMs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
