mlsanityData to 7 Oct 2026

IEPile: Unearthing Large-Scale Schema-Based Information Extraction Corpus

arXiv 2402.14710 ↗First posted 22 Feb 2024cs.CL cs.AI cs.DBcited by 5

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Abstract (arXiv)

Large Language Models (LLMs) demonstrate remarkable potential across various domains; however, they exhibit a significant performance gap in Information Extraction (IE). Note that high-quality instruction data is the vital key for enhancing the specific capabilities of LLMs, while current IE datasets tend to be small in scale, fragmented, and lack standardized schema. To this end, we introduce IEPile, a comprehensive bilingual (English and Chinese) IE instruction corpus, which contains approximately 0.32B tokens. We construct IEPile by collecting and cleaning 33 existing IE datasets, and introduce schema-based instruction generation to unearth a large-scale corpus. Experimentally, IEPile enhance the performance of LLMs for IE, with notable improvements in zero-shot generalization. We open-source the resource and pre-trained models, hoping to provide valuable support to the NLP community.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 53 of its 96 references that are in the map. First posted on arXiv 22 Feb 2024.

Builds on

53 of 96 references.

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38 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generationcited by 67
  2. 2024Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognitioncited by 5
  3. 2025Improving Natural Language Understanding for LLMs via Large-Scale Instruction Synthesiscited by 1
  4. 2025KnowCoder-V2: Deep Knowledge Analysiscited by 1
  5. 2025DeepJSONEval: Benchmarking Complex Nested JSON Data Mining for Large Language Modelscited by 0