10 citations · 18 across the 14 of their papers we have counts for
16 papers
Seeing Right but Saying Wrong: Inter- and Intra-Layer Refinement in MLLMs without Training
Shezheng Song, Shasha Li, Jie Yu
Multimodal Large Language Models (MLLMs) have demonstrated strong capabilities across a variety of vision-language tasks. However, their internal reasoning often exhibits a critica…
Where Does Vision Meet Language? Understanding and Refining Visual Fusion in MLLMs via Contrastive Attention
Shezheng Song, Shasha Li, Shan Zhao +6
Multimodal Large Language Models (MLLMs) have achieved remarkable progress in vision-language understanding, yet how they internally integrate visual and textual information remain…
Camel: Energy-Aware LLM Inference on Resource-Constrained Devices
Hao Xu, Long Peng, Shezheng Song +5
Most Large Language Models (LLMs) are currently deployed in the cloud, with users relying on internet connectivity for access. However, this paradigm faces challenges such as netwo…
EMSEdit: Efficient Multi-Step Meta-Learning-based Model Editing
Xiaopeng Li, Shasha Li, Xi Wang +7
Large Language Models (LLMs) power numerous AI applications, yet updating their knowledge remains costly. Model editing provides a lightweight alternative through targeted paramete…
How to Alleviate Catastrophic Forgetting in LLMs Finetuning? Hierarchical Layer-Wise and Element-Wise Regularization
Shezheng Song, Hao Xu, Jun Ma +5
Large Language Models (LLMs) exhibit strong general language capabilities. However, fine-tuning these models on domain-specific tasks often leads to catastrophic forgetting, where…
Rethinking Residual Distribution in Locate-then-Edit Model Editing
Xiaopeng Li, Shanwen Wang, Shasha Li +4
Model editing enables targeted updates to the knowledge of large language models (LLMs) with minimal retraining. Among existing approaches, locate-then-edit methods constitute a pr…
MOSABench: Multi-Object Sentiment Analysis Benchmark for Evaluating Multimodal Large Language Models Understanding of Complex Image
Shezheng Song, Chengxiang He, Shan Zhao +4
Multimodal large language models (MLLMs) have shown remarkable progress in high-level semantic tasks such as visual question answering, image captioning, and emotion recognition. H…
Exploring Large Language Models for Multimodal Sentiment Analysis: Challenges, Benchmarks, and Future Directions
Shezheng Song
Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to extract aspect terms and their corresponding sentiment polarities from multimodal information, including text and images.…
Identifying Knowledge Editing Types in Large Language Models
Xiaopeng Li, Shasha Li, Shangwen Wang +5
Knowledge editing has emerged as an efficient technique for updating the knowledge of large language models (LLMs), attracting increasing attention in recent years. However, there…
DIM: Dynamic Integration of Multimodal Entity Linking with Large Language Model
Shezheng Song, Shasha Li, Jie Yu +6
Our study delves into Multimodal Entity Linking, aligning the mention in multimodal information with entities in knowledge base. Existing methods are still facing challenges like a…
PTA: Enhancing Multimodal Sentiment Analysis through Pipelined Prediction and Translation-based Alignment
Shezheng Song, Shasha Li, Shan Zhao +8
Multimodal aspect-based sentiment analysis (MABSA) aims to understand opinions in a granular manner, advancing human-computer interaction and other fields. Traditionally, MABSA met…
DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking
Shezheng Song, Shasha Li, Shan Zhao +7
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
Xiaopeng Li, Shasha Li, Shezheng Song +8
The general capabilities of large language models (LLMs) make them the infrastructure for various AI applications, but updating their inner knowledge requires significant resources…
A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking
Shezheng Song, Shan Zhao, Chengyu Wang +4
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 a…
How to Bridge the Gap between Modalities: Survey on Multimodal Large Language Model
Shezheng Song, Xiaopeng Li, Shasha Li +5
We explore Multimodal Large Language Models (MLLMs), which integrate LLMs like GPT-4 to handle multimodal data, including text, images, audio, and more. MLLMs demonstrate capabilit…
PMET: Precise Model Editing in a Transformer
Xiaopeng Li, Shasha Li, Shezheng Song +3
Model editing techniques modify a minor proportion of knowledge in Large Language Models (LLMs) at a relatively low cost, which have demonstrated notable success. Existing methods…