16 citations · 37 across the 24 of their papers we have counts for
17 papers · 1 filter
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…
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…
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…
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…
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…
Dynamic Multi-View Fusion Mechanism For Chinese Relation Extraction
Jing Yang, Bin Ji, Shasha Li +3
Recently, many studies incorporate external knowledge into character-level feature based models to improve the performance of Chinese relation extraction. However, these methods te…
Span-based joint entity and relation extraction augmented with sequence tagging mechanism
Bin Ji, Shasha Li, Hao Xu +4
Span-based joint extraction simultaneously conducts named entity recognition (NER) and relation extraction (RE) in text span form. However, since previous span-based models rely on…
A Two-Phase Paradigm for Joint Entity-Relation Extraction
Bin Ji, Hao Xu, Jie Yu +4
An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task. However, these models sample a large number of negative e…
A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search
Huijun Liu, Jie Yu, Shasha Li +2
Textual adversarial attacks expose the vulnerabilities of text classifiers and can be used to improve their robustness. Existing context-aware methods solely consider the gold labe…
Few-shot Named Entity Recognition with Entity-level Prototypical Network Enhanced by Dispersedly Distributed Prototypes
Bin Ji, Shasha Li, Shaoduo Gan +3
Few-shot named entity recognition (NER) enables us to build a NER system for a new domain using very few labeled examples. However, existing prototypical networks for this task suf…
Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition
Bin Ji, Shasha Li, Jie Yu +2
For Named Entity Recognition (NER), sequence labeling-based and span-based paradigms are quite different. Previous research has demonstrated that the two paradigms have clear compl…
SummScore: A Comprehensive Evaluation Metric for Summary Quality Based on Cross-Encoder
Wuhang Lin, Shasha Li, Chen Zhang +4
Text summarization models are often trained to produce summaries that meet human quality requirements. However, the existing evaluation metrics for summary text are only rough prox…
Boosting Span-based Joint Entity and Relation Extraction via Squence Tagging Mechanism
Bin Ji, Shasha Li, Jie Yu +2
Span-based joint extraction simultaneously conducts named entity recognition (NER) and relation extraction (RE) in text span form. Recent studies have shown that token labels can c…