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Multi-step retrieval and reasoning improves radiology question answering with large language models
4
Zitationen
12
Autoren
2025
Jahr
Abstract
). Gains were largest in mid-sized and small models (e.g., Mistral Large: 72% → 81%), while very large models showed minimal change. RaR reduced hallucinations and provided clinically relevant evidence in 46% of cases, improving factual grounding. These results show that multi-step retrieval enhances diagnostic reliability, especially in deployable mid-sized LLMs. Code, datasets, and RaR are publicly available.
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