Search Results for author:
Found 8 papers, 5 papers with code
Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions
To this end, we construct a benchmark to evaluate the performance of LLMs on MABSA tasks and compare them with state-of-the-art supervised learning methods.
Identifying Knowledge Editing Types in Large Language Models
However, there is a lack of effective measures to prevent the malicious misuse of this technology, which could lead to harmful edits in LLMs.
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment
In contrast, a pipeline framework first identifies aspects through MATE (Multimodal Aspect Term Extraction) and then aligns these aspects with image patches for sentiment classification (MASC: Multimodal Aspect-Oriented Sentiment Classification).
Aspect-Based Sentiment Analysis
Multimodal Sentiment Analysis
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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: Survey on Multimodal Large Language Model
We explore Multimodal Large Language Models (MLLMs), which integrate LLMs like GPT-4 to handle multimodal data, including text, images, audio, and more.
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.
