mlsanityData to 7 Oct 2026

Document-Level Event Extraction with Definition-Driven ICL

arXiv 2408.05566 ↗First posted 10 Aug 2024cs.CL cs.AI cs.CYcited by 2

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Abstract (arXiv)

In the field of Natural Language Processing (NLP), Large Language Models (LLMs) have shown great potential in document-level event extraction tasks, but existing methods face challenges in the design of prompts. To address this issue, we propose an optimization strategy called "Definition-driven Document-level Event Extraction (DDEE)." By adjusting the length of the prompt and enhancing the clarity of heuristics, we have significantly improved the event extraction performance of LLMs. We used data balancing techniques to solve the long-tail effect problem, enhancing the model's generalization ability for event types. At the same time, we refined the prompt to ensure it is both concise and comprehensive, adapting to the sensitivity of LLMs to the style of prompts. In addition, the introduction of structured heuristic methods and strict limiting conditions has improved the precision of event and argument role extraction. These strategies not only solve the prompt engineering problems of LLMs in document-level event extraction but also promote the development of event extraction technology, providing new research perspectives for other tasks in the NLP field.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 36 references that are in the map. First posted on arXiv 10 Aug 2024.

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32 of 36 references.

References · 32

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20 more
  1. 2021Document-Level Event Argument Extraction by Conditional Generationcited by 154
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Citing papers per year: 2025: 2.

2 papers.

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