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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2025-03-31
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 [...]
2025-05-10
Five major obstacles are holding back RAG systems in healthcare
Retrieval-augmented generation (RAG) promises to help medical AI systems deliver up-to-date and reliable answers. But a new review shows that, so far, RAG rarely works as intended in real-world health [...]
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, [...]
2025-05-28
AI chatbots become dramatically less reliable in longer conversations, new study finds
A new study from Microsoft and Salesforce finds that even state-of-the-art AI language models become dramatically less reliable as conversations get longer and users reveal their requirements step by [...]
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 [...]