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evjang.com | ||
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www.shaped.ai
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| | | | | This article explores how cross-encoders, long praised for their performance in neural ranking, may in fact be reimplementing classic information retrieval logic, specifically, a semantic variant of BM25. Through mechanistic interpretability techniques, the authors uncover circuits within MiniLM that correspond to term frequency, IDF, length normalization, and final relevance scoring. The findings bridge modern transformer-based relevance modeling with foundational IR principles, offering both theoretical insight and a roadmap for building more transparent and interpretable neural retrieval systems. | |
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blog.evjang.com
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| | | | | Github repo here: https://github.com/ericjang/maml-jax Adaptive behavior in humans and animals occurs at many time scales: when I use a n... | |
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www.superannotate.com
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| | | | | Explore how reinforcement learning from AI feedback (RLAIF) streamlines AI training, enhancing efficiency without extensive human input. | |
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scorpil.com
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| | | In Part One of the "Understanding Generative AI" series, we delved into Tokenization - the process of dividing text into tokens, which serve as the fundamental units of information for neural networks. These tokens are crucial in shaping how AI interprets and processes language. Building upon this foundational knowledge, we are now ready to explore Neural Networks - the cornerstone technology underpinning all Artificial Intelligence research. A Short Look into the History Neural Networks, as a technology, have their roots in the 1940s and 1950s. | ||
