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
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 [...]
2025-03-31
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 [...]
2025-05-28
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 [...]
2025-06-07
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 [...]
2025-07-10
Most AI models can fake alignment, but safety training suppresses the behavior, study finds
A new study analyzing 25 language models finds that most do not fake safety compliance - though not due to a lack of capability.<br /> The article Most AI models can fake alignment, but safety t [...]
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 [...]