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Found 7 papers, 7 papers with code
IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus
Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE).
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs).
Beyond Isolation: Multi-Agent Synergy for Improving Knowledge Graph Construction
Knowledge graph construction (KGC) is a multifaceted undertaking involving the extraction of entities, relations, and events.
FactCHD: Benchmarking Fact-Conflicting Hallucination Detection
Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting hallucinations in real-world applications.
Making Language Models Better Tool Learners with Execution Feedback
To meet this need, we propose Tool leaRning wIth exeCution fEedback (TRICE), a two-stage end-to-end framework that enables the model to continually learn through feedback derived from tool execution, thereby learning when and how to use tools effectively.
InstructIE: A Bilingual Instruction-based Information Extraction Dataset
Large language models can perform well on general natural language tasks, but their effectiveness is still not optimal for information extraction.
Schema-adaptable Knowledge Graph Construction
This necessitates a system that can handle evolving schema automatically to extract information for KGC.
