Event extraction is an important task in natural language processing that focuses on mining
event-related information from unstructured text. Despite considerable advancements, it is …

Large language models (LLMs) usually fall short on information extraction (IE) tasks and
struggle to follow the complex instructions of IE tasks. This primarily arises from LLMs not …

Sharing knowledge between information extraction tasks has always been a challenge due
to the diverse data formats and task variations. Meanwhile, this divergence leads to …

Understanding events in texts is a core objective of natural language understanding, which
requires detecting event occurrences, extracting event arguments, and analyzing inter-event …

This article proposes a social simulation paradigm based on the GPT-3.5 large language
model. It involves constructing Generative Agents that emulate human cognition, memory …

Event understanding aims at understanding the content and relationship of events within
texts, which covers multiple complicated information extraction tasks: event detection, event …

Event extraction has gained considerable interest due to its wide-ranging applications.
However, recent studies draw attention to evaluation issues, suggesting that reported scores …

Event Factuality Detection (EFD) task determines the factuality of textual events, ie,
classifying whether an event is a fact, possibility, or impossibility, which is essential for …

Open Information Extraction (OIE) aims to extract objective structured knowledge from
natural texts, which has attracted growing attention to build dedicated models with human …

The increasing capability of large language models (LLMs) to generate fluent long-form texts
is presenting new challenges in distinguishing machine-generated outputs from human …