2025-03-30
Study finds that fewer documents can lead to better performance in RAG systems
Researchers at the Hebrew University of Jerusalem have discovered that the number of documents processed in Retrieval Augmented Generation (RAG) affects language model performance, even when the total text length remains constant.
The article Study finds that fewer documents can lead to better performance in RAG systems appeared first on THE DECODER.
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2025-05-10
Five major obstacles are holding back RAG systems in healthcare
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Less Is More: Why Retrieving Fewer Documents Can Improve AI Answers
Retrieval-Augmented Generation (RAG) is an approach to building AI systems that combines a language model with an external knowledge source. In simple terms, the AI first searches for relevant documen [...]
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Apple study finds "a fundamental scaling limitation" in reasoning models' thinking abilities
LLMs designed for reasoning, like Claude 3.7 and Deepseek-R1, are supposed to excel at complex problem-solving by simulating thought processes. But a new study by Apple researchers suggests that these [...]
2025-02-28
Engadget Podcast: iPhone 16e review and Amazon's AI-powered Alexa+
The keyword for the iPhone 16e seems to be "compromise." In this episode, Devindra chats with Cherlynn about her iPhone 16e review and try to figure out who this phone is actually for. Also, [...]
2025-04-22
So-called reasoning models are more efficient but not more capable than regular LLMs, study finds
A new study from Tsinghua University and Shanghai Jiao Tong University examines whether reinforcement learning with verifiable rewards (RLVR) helps large language models reason better—or simply make [...]