Summary of Iepile: Unearthing Large-scale Schema-based Information Extraction Corpus, by Honghao Gui et al.
IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus
by Honghao Gui, Lin Yuan, Hongbin Ye, Ningyu Zhang, Mengshu Sun, Lei Liang, Huajun Chen
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 paper introduces IEPile, a comprehensive bilingual (English and Chinese) Information Extraction (IE) instruction corpus, designed to enhance the performance of Large Language Models (LLMs) in IE tasks. The authors collect and clean 33 existing IE datasets, generating schema-based instructions to create a large-scale corpus containing approximately 0.32 billion tokens. Experimental results show that IEPile improves the performance of LLMs for IE, particularly in zero-shot generalization. The authors open-source the resource and pre-trained models, aiming to support the NLP community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a big difference in how computers understand information from text. Right now, machines are really good at some things, but not so great at others, like finding specific pieces of info. To help with this, the researchers created a huge collection of instructions that teach computers how to do IE better. They used lots of existing data and made it all work together in a special way. This new tool is super helpful for machines to learn from text, especially when they don’t have any extra information. |
Keywords
* Artificial intelligence * Generalization * Nlp * Zero shot
