Despite their impressive generative capabilities, LLMs are hindered by fact-conflicting
hallucinations in real-world applications. The accurate identification of hallucinations in texts …

Large Language Models (LLMs) demonstrate remarkable potential across various domains;
however, they exhibit a significant performance gap in Information Extraction (IE). Note that …

Large Language Models (LLMs) demonstrate remarkable potential across various domains;
however, they exhibit a significant performance gap in Information Extraction (IE). Note that …

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 …

Large Language Models (LLMs), such as ChatGPT/GPT-4, have garnered widespread
attention owing to their myriad of practical applications, yet their adoption has been constrained …

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 the main …

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 …

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 high-…

Knowledge graph construction (KGC) is a multifaceted undertaking involving the extraction
of entities, relations, and events. Traditionally, large language models (LLMs) have been …

Quantum dot light‐emitting diodes (QD‐LEDs) are highly promising light sources with excellent
figures of merit. Although great successes have been achieved in elevating some key …