A downside with using VLMs to parse PDFs is guaranteeing that the output text is *correct* and outpu...
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1️⃣ Text correctness: making sure that digits, words, sentences are not hallucinated or dropped. 2️⃣ Reading Order: making sure that complex https://t.co/IFZXPZ37sb" / X
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A downside with using VLMs to parse PDFs is guaranteeing that the output text is *correct* and output in the correct reading order. !Image 1: 1️⃣ Text correctness: making sure that digits, words, sentences are not hallucinated or dropped. !Image 2: 2️⃣ Reading Order: making sure that complex multi-layout pages are linearized into the right 1-d text order. We call this Content Faithfulness in ParseBench, our comprehensive document OCR benchmark for agents. We have 167k rules that measure digit/word/sentence-level correctness along with reading order correctness. It seems relatively table-stakes, but no parser gets this 100% right, and this means that the agent’s downstream decision-making is compromised. Come learn more about how this metric works in the video below, along with our full blog writeup, whitepaper, and website! Blog: llamaindex.ai/blog/parsebenc Paper: arxiv.org/abs/2604.08538 Website: parsebench.ai/?utm_medium=so
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LlamaIndex !Image 4: 🦙
@llama_index
Apr 17
Let's talk content faithfulness. Four days ago, we launched ParseBench, the first document OCR benchmark for AI agents. Its most fundamental metric asks: did the parser capture all the text, in order, without making things up? We grade three failure modes with 167K+ rule-based
