5 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…