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Evaluating OCR-to-Markdown Systems Is Fundamentally Broken (and Why That’s Hard to Fix)

Uncategorised12 hours ago1View0Likes0Comments
Evaluating OCR systems that convert PDFs or document images into Markdown is far more complex than it appears. Unlike plain text OCR, OCR-to-Markdown requires models to recover content, layout, reading order, and representation choices simultaneously. Today’s benchmarks attempt to score this with a mix of string matching, heuristic alignment, and format-specific rules—but in practice, these…

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5 Useful Python Scripts for Effective Feature Engineering

Data Analytics3 days ago4Views0Likes0Comments
Image by Author   #  Introduction   As a machine learning practitioner, you know that feature engineering is painstaking, manual work. You need to create interaction terms between features, encode categorical variables properly, extract temporal patterns from dates, generate aggregations, and transform distributions. For each potential feature, you test whether it improves model performance, iterate…

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FACTS Benchmark Suite: a new way to systematically evaluate LLMs factuality

OpenAI3 days ago4Views0Likes0Comments
Large language models (LLMs) are increasingly becoming a primary source for information delivery across diverse use cases, so it’s important that their responses are factually accurate. In order to continue improving their performance on this industry-wide challenge, we have to better understand the types of use cases where models struggle to provide an accurate response…

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Gemma Scope 2: Helping the AI Safety Community Deepen Understanding of Complex Language Model Behavior

OpenAIDecember 29, 202516Views0Likes0Comments
Announcing a new, open suite of tools for language model interpretability Large Language Models (LLMs) are capable of incredible feats of reasoning, yet their internal decision-making processes remain largely opaque. Should a system not behave as expected, a lack of visibility into its internal workings can make it difficult to pinpoint the exact reason for…

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