Summary of Decompose, Enrich, and Extract! Schema-aware Event Extraction Using Llms, by Fatemeh Shiri et al.
Decompose, Enrich, and Extract! Schema-aware Event Extraction using LLMs
by Fatemeh Shiri, Van Nguyen, Farhad Moghimifar, John Yoo, Gholamreza Haffari, Yuan-Fang Li
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method in this paper introduces a novel approach to harnessing Large Language Models (LLMs) for automated Event Extraction, addressing concerns over hallucination by decomposing the task into Event Detection and Event Argument Extraction. The method integrates dynamic schema-aware augmented retrieval examples into prompts tailored for each specific inquiry, extending advanced prompting techniques like Retrieval-Augmented Generation. Evaluation on prominent event extraction benchmarks and a synthesized benchmark shows superior performance compared to baseline approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special language models to extract information from text and help people make decisions. The problem is that these models can sometimes get things wrong and provide false information. To fix this, the researchers broke down the task of extracting events into two parts: detecting events and finding the details about those events. They also added extra information to the prompts given to the model to help it focus on specific topics and reduce errors. |
Keywords
» Artificial intelligence » Event detection » Hallucination » Prompting » Retrieval augmented generation
