16 citations · 38 across the 28 of their papers we have counts for
8 papers · 1 filter
Model Editing for LLMs4Code: How Far are We?
Xiaopeng Li, Shangwen Wang, Shasha Li +6
Large Language Models for Code (LLMs4Code) have been found to exhibit outstanding performance in the software engineering domain, especially the remarkable performance in coding ta…
LSAQ: Layer-Specific Adaptive Quantization for Large Language Model Deployment
Binrui Zeng, Bin Ji, Xiaodong Liu +7
As Large Language Models (LLMs) demonstrate exceptional performance across various domains, deploying LLMs on edge devices has emerged as a new trend. Quantization techniques, whic…
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…
EMP: Enhance Memory in Data Pruning
Jinying Xiao, Ping Li, Jie Nie +6
Recently, large language and vision models have shown strong performance, but due to high pre-training and fine-tuning costs, research has shifted towards faster training via datas…
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…