Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs
This addresses hallucination issues in event extraction for applications like situational awareness, though it is incremental as it builds on existing prompting techniques.
The paper tackles hallucination in LLM-based event extraction by decomposing the task into detection and argument extraction and using dynamic schema-aware retrieval-augmented prompts, achieving superior performance on benchmarks compared to baselines.
Large Language Models (LLMs) demonstrate significant capabilities in processing natural language data, promising efficient knowledge extraction from diverse textual sources to enhance situational awareness and support decision-making. However, concerns arise due to their susceptibility to hallucination, resulting in contextually inaccurate content. This work focuses on harnessing LLMs for automated Event Extraction, introducing a new method to address hallucination by decomposing the task into Event Detection and Event Argument Extraction. Moreover, the proposed method integrates dynamic schema-aware augmented retrieval examples into prompts tailored for each specific inquiry, thereby extending and adapting advanced prompting techniques such as Retrieval-Augmented Generation. Evaluation findings on prominent event extraction benchmarks and results from a synthesized benchmark illustrate the method's superior performance compared to baseline approaches.