16 citations · 20 across the 10 of their papers we have counts for
11 papers
Collective Bias Mitigation via Model Routing and Collaboration
Mingzhe Du, Luu Anh Tuan, Xiaobao Wu +7
Large language models (LLMs) are increasingly deployed in public health, finance, and governance, requiring both accuracy and societal value alignment. Despite recent advances, LLM…
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…
Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning
Xiaohu Du, Ming Wen, Jiahao Zhu +5
Code Pre-trained Models (CodePTMs) based vulnerability detection have achieved promising results over recent years. However, these models struggle to generalize as they typically l…
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…
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…
Topic-Grained Text Representation-based Model for Document Retrieval
Mengxue Du, Shasha Li, Jie Yu +5
Document retrieval enables users to find their required documents accurately and quickly. To satisfy the requirement of retrieval efficiency, prevalent deep neural methods adopt a…
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…