资源受限下的联合多模态情感推理与分类：基于思维链增强与蒸馏
Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation
AI总结：
针对资源受限下多模态情感推理与分类任务，提出基于“教师-助手-学生”蒸馏范式的MulCoT-RD模型，以30亿参数实现高效推理与分类，兼具强泛化性与可解释性。
摘要：
社交媒体平台上丰富多模态内容的激增极大地推动了多模态情感分析（MSA）的发展，而大型语言模型（LLMs）进一步加速了该领域的进展。当前方法主要利用参数庞大的（多模态）LLMs的知识和推理能力进行情感分类，忽视了在资源受限环境中自主生成多模态情感推理的能力。因此，我们聚焦于资源受限下的联合多模态情感推理与分类任务（JMSRC），该任务仅使用轻量级模型同时进行多模态情感推理链生成和情感分类。我们提出了一种面向JMSRC的多模态思维链推理蒸馏模型MulCoT-RD，该模型采用“教师-助手-学生”蒸馏范式，以应对资源受限环境中的部署约束。我们首先利用高性能多模态大语言模型（MLLM）生成初始推理数据集，并通过多任务学习机制训练一个中等规模的助手模型。随后，联合训练一个轻量级学生模型，以高效执行多模态情感推理生成与分类。在四个数据集上的大量实验表明，仅拥有30亿参数的MulCoT-RD在JMSRC上取得了强劲性能，同时展现出稳健的泛化能力和增强的可解释性。
英文摘要：
Current approaches for Multimodal Sentiment Analysis (MSA) primarily leverage the knowledge and reasoning capabilities of parameter-heavy (Multimodal) LLMs for classification, overlooking autonomous multimodal sentiment reasoning generation in resource-constrained environments. In this paper, we focus on the Resource-Limited Joint Multimodal Sentiment Reasoning and Classification task, JMSRC, which simultaneously performs multimodal sentiment reasoning chain generation and sentiment classification only with a lightweight model. We propose a Multimodal Chain-of-Thought Reasoning Distillation model, MulCoT-RD, designed for JMSRC that employs a "Teacher-Assistant-Student" distillation paradigm to address deployment constraints in resource-limited environments. We first leverage a high-performance Multimodal Large Language Model (MLLM) to generate the initial reasoning dataset and train a medium-sized assistant model with a multi-task learning mechanism. A lightweight student model is jointly trained to perform efficient multimodal sentiment reasoning generation and classification. Extensive experiments on four datasets demonstrate that MulCoT-RD, with only 3B parameters, achieves strong performance on JMSRC while exhibiting robust generalization and enhanced interpretability.
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。
