5 citations · 9 across the 9 of their papers we have counts for
11 papers
EventSum: A Large-Scale Event-Centric Summarization Dataset for Chinese Multi-News Documents
Mengna Zhu, Kaisheng Zeng, Mao Wang +4
In real life, many dynamic events, such as major disasters and large-scale sports events, evolve continuously over time. Obtaining an overview of these events can help people quick…
LLMAEL: Large Language Models are Good Context Augmenters for Entity Linking
Amy Xin, Yunjia Qi, Zijun Yao +5
Specialized entity linking (EL) models are well-trained at mapping mentions to unique knowledge base (KB) entities according to a given context. However, specialized EL models stru…
CMNEE: A Large-Scale Document-Level Event Extraction Dataset based on Open-Source Chinese Military News
Mengna Zhu, Zijie Xu, Kaisheng Zeng +4
Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis…
Event-level Knowledge Editing
Hao Peng, Xiaozhi Wang, Chunyang Li +5
Knowledge editing aims at updating knowledge of large language models (LLMs) to prevent them from becoming outdated. Existing work edits LLMs at the level of factual knowledge trip…
When does In-context Learning Fall Short and Why? A Study on Specification-Heavy Tasks
Hao Peng, Xiaozhi Wang, Jianhui Chen +8
In-context learning (ICL) has become the default method for using large language models (LLMs), making the exploration of its limitations and understanding the underlying causes cr…
MAVEN-Arg: Completing the Puzzle of All-in-One Event Understanding Dataset with Event Argument Annotation
Xiaozhi Wang, Hao Peng, Yong Guan +9
Understanding events in texts is a core objective of natural language understanding, which requires detecting event occurrences, extracting event arguments, and analyzing inter-eve…