Back to search

Article

Streamlined Document-Level Event Causality Identification with Large Language Models

2025-04-15

Abstract excerpt

Document-level event causality identification (DECI) is crucial for deep text understanding, yet traditional methods struggle with error propagation, neglect document structure, and incur high computational costs. This paper introduces Prompt-based Structure-Aware Causal Identification (PSACI), a novel approach leveraging Large Language Models (LLMs) through carefully designed prompts. PSACI implicitly captures do...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
f8da8086-189b-5bd4-99af-1a29b2bddd28
DOI
10.20944/preprints202504.1229.v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Streamlined Document-Level Event Causality Identification with Large Language ModelsDOI 10.20944/preprints202504.1229.v1
Select a neighboring publication to make it the new centre.