An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability

• URL: http://arxiv.org/abs/2505.16193v1
• Date: Thu, 22 May 2025 03:51:41 GMT
• Title: An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability
• Authors: Daiqing Wu, Dongbao Yang, Sicheng Zhao, Can Ma, Yu Zhou,
• Abstract summary: We extend the zero-shot paradigm to In-Context Learning (ICL) and conduct an in-depth study on configuring demonstrations.<n>Specifically, three key factors that cover demonstrations' retrieval, presentation, and distribution are investigated and optimized.<n>A predictive bias inherent in MLLMs is also discovered and later effectively counteracted.
• Score: 20.760483719891887
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advancements in Multimodal Large Language Models (MLLMs) have enabled various multimodal tasks to be addressed under a zero-shot paradigm. This paradigm sidesteps the cost of model fine-tuning, emerging as a dominant trend in practical application. Nevertheless, Multimodal Sentiment Analysis (MSA), a pivotal challenge in the quest for general artificial intelligence, fails to accommodate this convenience. The zero-shot paradigm exhibits undesirable performance on MSA, casting doubt on whether MLLMs can perceive sentiments as competent as supervised models. By extending the zero-shot paradigm to In-Context Learning (ICL) and conducting an in-depth study on configuring demonstrations, we validate that MLLMs indeed possess such capability. Specifically, three key factors that cover demonstrations' retrieval, presentation, and distribution are comprehensively investigated and optimized. A sentimental predictive bias inherent in MLLMs is also discovered and later effectively counteracted. By complementing each other, the devised strategies for three factors result in average accuracy improvements of 15.9% on six MSA datasets against the zero-shot paradigm and 11.2% against the random ICL baseline.

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