Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation

ACL ARR 2026 January Submission1298 Authors

Keywords: Joint Multimodal Sentiment Reasoning and Classification, Multimodal Chain-of-Thought Enhancement, Reasoning Distillation

Abstract:

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 and achieves strong performance on JMSRC, while exhibiting robust generalization and enhanced interpretability.

Paper Type: Long

Research Area: Low-resource Methods for NLP

Research Area Keywords: Mulmodal Sentiment Analysis, Multimdal Sentiment Reasoning, Limited Source

Contribution Types: Model analysis & interpretability, Approaches to low-resource settings, Approaches low compute settings-efficiency

Languages Studied: English

Submission Number: 1298