LlamaIndex 发布 ExtractBench Pro：文档提取基准

Document extraction is an arbitrarily complex reasoning problem for both frontier models and humans. ✅ The schema encodes essentially hundreds of interrelated reasoning tasks ✅ A PDF can be hundreds or even thousands of pages - it’s an open-world information environment for LLMs to explore (similar to why gdp.pdf is hard) ✅ Solving even a single task is a hard reasoning problem over this environment, where the right dependencies must be reasoned over ✅ The information can be scattered across extraordinarily complex dimensions, from sections, tables, charts, diagrams, handwriting, and more. Document work is a microcosm of knowledge work as a whole. We’re invested in developing the hardest benchmarks that reflect real-world professional work, challenge the capabilities of frontier models, and incentivize specialized model development for document work. We released ExtractBench Pro today. Check out the below example as an example of a reasoning task that requires finding the right constraints, then pulling information from tables. llamaindex.ai/blog/introduci… ExtractBench Pro: extractbenchpro.ai Jerry Liu @jerryjliu0 Introducing ExtractBenchPro: the most challenging benchmark for real-world document extraction. We see a massive long tail of document complexity in terms of use cases brought on from our customers, that are completely unsolved from a price-performance standpoint. These include trial balances, well logs, canvass binders, site plan reviews, duty reconciliations, loss runs, and more. Most existing extraction benchmarks are saturated by the frontier models (our cheapest modes now score 93%+ on ExtractBench). So there is enormous economic value in building a complex extraction benchmark that reflects this long-tail of knowledge work. We spent a long time curating the most complex elements that we saw from real world extraction use cases: ✅ Document conventions: containing legends or rules where the rest of the document should be interpreted ✅ Density of internal jargon: specific definitions that must be cross-referenced within a doc ✅ Aggregation and reasoning: beyond returning exact values on a page, infer values derived from aggregating multiple sources of information ✅ Drawn relations: complex diagrams like procedures and circuits ✅ Charts inside documents: read precise points within a chart ✅ Long chart sheets: some data series like well logs cram all this data into an extremely long page Within the industries where document extraction is relevant, even small mistakes can have enormous negative impacts. Therefore our headline evaluation metric is based on perfect documents: the proportion of docs with perfect (100%) value f1 score. (Besides this we also measure value f1 and grounded f1) It’s an extremely hard task for even the latest frontier models to solve, topping out at ~58% or so in Value F1 score for Codex with Astra - which is quite good but extremely expensive (almost $1 per page!) Our LlamaParse agentic/agentic plus modes are 20-40% and cover the left side of the Pareto frontier, at price ranges that are feasible for full production use. ExtractBenchPro is available today as a website/technical blog, and also on Github and HuggingFace. Check it out! Blog: llamaindex.ai/blog/introduci… Website: extractbenchpro.ai Hugging Face: huggingface.co/datasets/llama… Github (is ExtractBench): github.com/run-llama/Extr… Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet 💬 2 🔄 2 ❤️ 4 👀 651 📊 3 ⚡