Summary of Unimel: a Unified Framework For Multimodal Entity Linking with Large Language Models, by Liu Qi et al.
UniMEL: A Unified Framework for Multimodal Entity Linking with Large Language Models
by Liu Qi, He Yongyi, Lian Defu, Zheng Zhi, Xu Tong, Liu Che, Chen Enhong
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed UniMEL framework addresses the Multimodal Entity Linking (MEL) task, which aims to connect ambiguous mentions in multimodal contexts to referent entities in a knowledge base. Existing methods rely on complex mechanisms and extensive model tuning, overlooking visual semantic information and struggling with textual ambiguity, redundancy, and noisy images. The advent of Large Language Models (LLMs), particularly Multimodal LLMs, provides new insights into addressing this challenge. UniMEL establishes a new paradigm by processing MEL tasks using LLMs, integrating textual and visual information to refine entity representations and employ embedding-based methods for candidate retrieval and re-ranking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The UniMEL framework is a unified approach that uses Large Language Models (LLMs) to process multimodal entity linking tasks. It integrates textual and visual information to refine entity representations and employs an embedding-based method for retrieving and re-ranking candidate entities. The framework achieves state-of-the-art performance on three public benchmark datasets, demonstrating its effectiveness in addressing the MEL task. |
Keywords
» Artificial intelligence » Embedding » Entity linking » Knowledge base
