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Found 5 papers, 4 papers with code
DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking
Multimodal entity linking (MEL) aims to utilize multimodal information (usually textual and visual information) to link ambiguous mentions to unambiguous entities in knowledge base.
SWEA: Updating Factual Knowledge in Large Language Models via Subject Word Embedding Altering
In particular, local editing methods, which directly update model parameters, are more suitable for updating a small amount of knowledge.
A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking
Multimodal Entity Linking (MEL) aims at linking ambiguous mentions with multimodal information to entity in Knowledge Graph (KG) such as Wikipedia, which plays a key role in many applications.
How to Bridge the Gap between Modalities: A Comprehensive Survey on Multimodal Large Language Model
The study surveys existing modal alignment methods in MLLMs into four groups: (1) Multimodal Converters that change data into something LLMs can understand; (2) Multimodal Perceivers to improve how LLMs perceive different types of data; (3) Tools Assistance for changing data into one common format, usually text; and (4) Data-Driven methods that teach LLMs to understand specific types of data in a dataset.
PMET: Precise Model Editing in a Transformer
To achieve more precise model editing, we analyze hidden states of MHSA and FFN, finding that MHSA encodes certain general knowledge extraction patterns.
