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    LlamaIndex Launches ExtractBench on Kaggle

    New benchmark tests schema-guided extraction on complex enterprise documents and scans.

    JL
    KA
    L🦙
    4 Sources, 28d ago, first seen 28d ago

    TLDR

    The official LlamaIndex account announced that ExtractBench is now live on Kaggle. The benchmark evaluates schema-guided document extraction on files that commonly disrupt downstream agents and workflows. It focuses on long record lists, noisy scans, handwriting, and complex tables. The effort draws from 370 enterprise documents spanning 8 business domains and 67 document types. A light gray promotional graphic accompanied the post. The announcement presents the benchmark as a public resource for testing extraction performance on challenging real-world materials.

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    4 Sources, first seen 28d ago

    Combined views

    24.5K

    4 Sources, first seen 28d ago

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    16 comments
    52 saves
    22 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    4 Sources

    @llama_indexExtractBench is now live on @Kaggle. It tests schema-guided document extraction on the documents most likely to break downstream agents and workflows, including long record lists, noisy scans, handwriting, and complex tables. The benchmark covers 370 enterprise documents across 8 business domains and 67 document types. See how LlamaParse, Codex, Claude Code, and more stack up. Read the full story →https://www.llamaindex.ai/blog/llamaindex-and-kaggle-launch-a-document-extraction-leaderboard-for-ai-agents
    @jerryjliu0RT @llama_index: ExtractBench is now live on @Kaggle. It tests schema-guided document extraction on the documents most likely to break dow…
    @kaggleIntroducing ExtractBench on Kaggle Benchmarks with @llama_index. When AI agents rely on schema-guided extraction before human review, one truncated schedule or invented value becomes a wrong payment or decision. ExtractBench evaluates models in workflows based on real-world documents across industries such as supply chain, healthcare, and finance, by measuring whether the system returns: ➣ Missing fields as null instead of inventing a value ➣ Source evidence for each value ➣ Every record of each repeated structure GPT-5.6 Sol currently leads at 91%.

    4 Sources

    @llama_indexExtractBench is now live on @Kaggle. It tests schema-guided document extraction on the documents most likely to break downstream agents and workflows, including long record lists, noisy scans, handwriting, and complex tables. The benchmark covers 370 enterprise documents across 8 business domains and 67 document types. See how LlamaParse, Codex, Claude Code, and more stack up. Read the full story →https://www.llamaindex.ai/blog/llamaindex-and-kaggle-launch-a-document-extraction-leaderboard-for-ai-agents
    @jerryjliu0RT @llama_index: ExtractBench is now live on @Kaggle. It tests schema-guided document extraction on the documents most likely to break dow…
    @kaggleIntroducing ExtractBench on Kaggle Benchmarks with @llama_index. When AI agents rely on schema-guided extraction before human review, one truncated schedule or invented value becomes a wrong payment or decision. ExtractBench evaluates models in workflows based on real-world documents across industries such as supply chain, healthcare, and finance, by measuring whether the system returns: ➣ Missing fields as null instead of inventing a value ➣ Source evidence for each value ➣ Every record of each repeated structure GPT-5.6 Sol currently leads at 91%.