Prompting ChatGPT in MNER: Enhanced Multimodal Named Entity Recognition with Auxiliary Refined Knowledge

TL;DR

This paper introduces PGIM, a two-stage framework that leverages ChatGPT as an implicit knowledge base to generate auxiliary knowledge, significantly improving multimodal named entity recognition on social media data.

Contribution

The paper proposes PGIM, a novel approach that uses ChatGPT to heuristically generate auxiliary knowledge, enhancing MNER performance and robustness compared to existing methods.

Findings

Outperforms state-of-the-art MNER methods on two datasets

Demonstrates improved robustness and generalization

Effectively leverages ChatGPT for auxiliary knowledge generation

Abstract

Multimodal Named Entity Recognition (MNER) on social media aims to enhance textual entity prediction by incorporating image-based clues. Existing studies mainly focus on maximizing the utilization of pertinent image information or incorporating external knowledge from explicit knowledge bases. However, these methods either neglect the necessity of providing the model with external knowledge, or encounter issues of high redundancy in the retrieved knowledge. In this paper, we present PGIM -- a two-stage framework that aims to leverage ChatGPT as an implicit knowledge base and enable it to heuristically generate auxiliary knowledge for more efficient entity prediction. Specifically, PGIM contains a Multimodal Similar Example Awareness module that selects suitable examples from a small number of predefined artificial samples. These examples are then integrated into a formatted prompt template tailored to the MNER and guide ChatGPT to generate auxiliary refined knowledge. Finally, the acquired knowledge is integrated with the original text and fed into a downstream model for further processing. Extensive experiments show that PGIM outperforms state-of-the-art methods on two classic MNER datasets and exhibits a stronger robustness and generalization capability.