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 blog.reachsumit.com
Zero and Few Shot Text Retrieval and Ranking Using Large Language Models - Sumit's Diary
| | magazine.sebastianraschka.com
Tips for LLM Pretraining and Evaluating Reward Models
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| | I'm an LLM Research Engineer with over a decade of experience in artificial intelligence. My work bridges academia and industry, with roles including senior staff at an AI company and a statistics professor. My expertise lies in LLM research and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations.
| | www.shaped.ai
Cross-Encoder Rediscovers a Semantic Variant of BM25 | Shaped Blog
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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.
| | weaviate.io
Ranking Models for Better Search | Weaviate
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| | Learn about the different ranking models that are used for better search.
| | neuralnetworksanddeeplearning.com
Neural networks and deep learning
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| [AI summary] The text provides an in-depth explanation of the backpropagation algorithm in neural networks. It starts by discussing the concept of how small changes in weights propagate through the network to affect the final cost, leading to the derivation of the partial derivatives required for gradient descent. The explanation includes a heuristic argument based on tracking the perturbation of weights through the network, resulting in a chain of partial derivatives. The text also touches on the historical context of how backpropagation was discovered, emphasizing the process of simplifying complex proofs and the role of using weighted inputs (z-values) as intermediate variables to streamline the derivation. Finally, it concludes with a citation and licens...