26 citations · 75 across the 17 of their papers we have counts for
17 papers
Why Do Open-Source LLMs Struggle with Data Analysis? A Systematic Empirical Study
Yuqi Zhu, Yi Zhong, Jintian Zhang +7
Large Language Models (LLMs) hold promise in automating data analysis tasks, yet open-source models face significant limitations in these kinds of reasoning-intensive scenarios. In…
Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentation
Derong Xu, Xinhang Li, Ziheng Zhang +7
Large Language Models (LLMs) demonstrate remarkable capabilities, yet struggle with hallucination and outdated knowledge when tasked with complex knowledge reasoning, resulting in…
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding
Derong Xu, Ziheng Zhang, Zhihong Zhu +7
The impressive capabilities of large language models (LLMs) have attracted extensive interests of applying LLMs to medical field. However, the complex nature of clinical environmen…
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models
Derong Xu, Ziheng Zhang, Zhenxi Lin +6
Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs) by making predictions for missing links. Description-based KGC leverages…
Editing Factual Knowledge and Explanatory Ability of Medical Large Language Models
Derong Xu, Ziheng Zhang, Zhihong Zhu +9
Model editing aims to precisely alter the behaviors of large language models (LLMs) in relation to specific knowledge, while leaving unrelated knowledge intact. This approach has p…
Biomedical Entity Linking as Multiple Choice Question Answering
Zhenxi Lin, Ziheng Zhang, Xian Wu +1
Although biomedical entity linking (BioEL) has made significant progress with pre-trained language models, challenges still exist for fine-grained and long-tailed entities. To addr…
Enhancing Large Language Model with Decomposed Reasoning for Emotion Cause Pair Extraction
Jialiang Wu, Yi Shen, Ziheng Zhang +1
Emotion-Cause Pair Extraction (ECPE) involves extracting clause pairs representing emotions and their causes in a document. Existing methods tend to overfit spurious correlations,…
Improving Biomedical Entity Linking with Retrieval-enhanced Learning
Zhenxi Lin, Ziheng Zhang, Xian Wu +1
Biomedical entity linking (BioEL) has achieved remarkable progress with the help of pre-trained language models. However, existing BioEL methods usually struggle to handle rare and…
Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network
Yongqi Zhang, Quanming Yao, Ling Yue +4
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which offer possibilities for treating and alleviating diseases, with computational methods can improve patie…
Relation-aware Ensemble Learning for Knowledge Graph Embedding
Ling Yue, Yongqi Zhang, Quanming Yao +5
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In thi…
Prompt Combines Paraphrase: Teaching Pre-trained Models to Understand Rare Biomedical Words
Haochun Wang, Chi Liu, Nuwa Xi +7
Prompt-based fine-tuning for pre-trained models has proven effective for many natural language processing tasks under few-shot settings in general domain. However, tuning with prom…
Multi-modal Contrastive Representation Learning for Entity Alignment
Zhenxi Lin, Ziheng Zhang, Meng Wang +3
Multi-modal entity alignment aims to identify equivalent entities between two different multi-modal knowledge graphs, which consist of structural triples and images associated with…
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
Hengyi Zheng, Rui Wen, Xi Chen +7
Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer fro…
Unsupervised Knowledge Graph Alignment by Probabilistic Reasoning and Semantic Embedding
Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen +4
Knowledge Graph (KG) alignment is to discover the mappings (i.e., equivalent entities, relations, and others) between two KGs. The existing methods can be divided into the embeddin…
PRASEMap: A Probabilistic Reasoning and Semantic Embedding based Knowledge Graph Alignment System
Zhiyuan Qi, Ziheng Zhang, Jiaoyan Chen +2
Knowledge Graph (KG) alignment aims at finding equivalent entities and relations (i.e., mappings) between two KGs. The existing approaches utilize either reasoning-based or semanti…
OntoEA: Ontology-guided Entity Alignment via Joint Knowledge Graph Embedding
Yuejia Xiang, Ziheng Zhang, Jiaoyan Chen +3
Semantic embedding has been widely investigated for aligning knowledge graph (KG) entities. Current methods have explored and utilized the graph structure, the entity names and att…
An Industry Evaluation of Embedding-based Entity Alignment
Ziheng Zhang, Jiaoyan Chen, Xi Chen +4
Embedding-based entity alignment has been widely investigated in recent years, but most proposed methods still rely on an ideal supervised learning setting with a large number of u…