8 citations · 25 across the 8 of their papers we have counts for
8 papers
Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesis
Lin Yuan, Jun Xu, Honghao Gui +4
High-quality, large-scale instructions are crucial for aligning large language models (LLMs), however, there is a severe shortage of instruction in the field of natural language un…
IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus
Honghao Gui, Lin Yuan, Hongbin Ye +4
Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE). Note that…
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
Yixin Ou, Ningyu Zhang, Honghao Gui +9
In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs). To construct hig…
Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction
Hongbin Ye, Honghao Gui, Aijia Zhang +2
This paper introduces CooperKGC, a novel framework challenging the conventional solitary approach of large language models (LLMs) in knowledge graph construction (KGC). CooperKGC e…
FactCHD: Benchmarking Fact-Conflicting Hallucination Detection
Xiang Chen, Duanzheng Song, Honghao Gui +7
Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications. The accurate identification of hallucinations in…
Making Language Models Better Tool Learners with Execution Feedback
Shuofei Qiao, Honghao Gui, Chengfei Lv +3
Tools serve as pivotal interfaces that enable humans to understand and reshape the environment. With the advent of foundation models, AI systems can utilize tools to expand their c…
InstructIE: A Bilingual Instruction-based Information Extraction Dataset
Honghao Gui, Shuofei Qiao, Jintian Zhang +6
Large language models can perform well on general natural language tasks, but their effectiveness is still suboptimal for information extraction (IE). Recent works indicate that th…
Schema-adaptable Knowledge Graph Construction
Hongbin Ye, Honghao Gui, Xin Xu +3
Conventional Knowledge Graph Construction (KGC) approaches typically follow the static information extraction paradigm with a closed set of pre-defined schema. As a result, such ap…