ScrapeGraphAI-100k: Dataset for Schema-Constrained LLM Generation

📰 ArXiv cs.AI

Learn about ScrapeGraphAI-100k, a dataset for schema-constrained LLM generation, and how to apply it to improve tool use and structured extraction in large language models

advanced Published 11 May 2026
Action Steps
  1. Collect and preprocess the ScrapeGraphAI-100k dataset using Python and relevant libraries
  2. Apply schema-constrained generation techniques to the dataset using LLMs and evaluate their performance
  3. Use the dataset to fine-tune LLMs for tool use and structured extraction tasks, such as JSON schema generation
  4. Compare the performance of different LLMs on the dataset using metrics like accuracy and F1-score
  5. Integrate the dataset into existing NLP pipelines to improve knowledge base construction and information extraction
Who Needs to Know This

NLP engineers and researchers can benefit from this dataset to develop and fine-tune their models for schema-constrained generation tasks, while data scientists can utilize it to improve knowledge base construction and structured extraction

Key Insight

💡 ScrapeGraphAI-100k provides a large-scale dataset for schema-constrained LLM generation, enabling researchers and practitioners to develop more accurate and efficient models for tool use and structured extraction

Share This
🚀 Introducing ScrapeGraphAI-100k, a dataset for schema-constrained LLM generation! 🤖 Improve tool use and structured extraction in your NLP models with this new resource 📈

Key Takeaways

Learn about ScrapeGraphAI-100k, a dataset for schema-constrained LLM generation, and how to apply it to improve tool use and structured extraction in large language models

Full Article

Title: ScrapeGraphAI-100k: Dataset for Schema-Constrained LLM Generation

Abstract:
arXiv:2602.15189v2 Announce Type: replace-cross Abstract: Producing output that conforms to a specified JSON schema underlies tool use, structured extraction, and knowledge base construction in modern large language models. Despite this centrality, public datasets for the task remain small, synthetic, or text-only, and rarely pair real page content with the prompts and schemas used in practice. We introduce ScrapeGraphAI-100k, 93,695 schema-constrained extraction events collected via opt-in Scra
Read full paper → ← Back to Reads

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