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Teaching Through Analogies: A Modular Pipeline for Educational Analogy Generation Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation Guarded Repair for Harm-Aware Post-hoc Replacement of LLM Mathematical Reasoning Mimir: Large-scale Multilingual Concept Modeling STREAM: A Data-Centric Framework for Mining High-Value Task-Oriented Dialogues from Streaming Media Evidence-Linked Radiology Reporting: A Human-Supervised Reference Architecture for Structured Imaging Intelligence Overcoming "Physics Shock" in Earth Observation A Heteroscedastic Uncertainty Framework for PINN-based Flood Inference TGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism Beyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs A Large-Scale Dataset and Benchmark: Do Protein-Ligand Models Learn Binding Sites or Just Binding Likelihood? 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Retrieval-Augmented Large Language Models for Evidence-Informed Guidance on Cannabidiol Use in Older Adults
2026-04-14 · via cs.AI updates on arXiv.org

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Abstract:Older adults commonly experience chronic conditions such as pain and sleep disturbances and may consider cannabidiol for symptom management. Safe use requires appropriate dosing, careful titration, and awareness of drug interactions, yet stigma and limited health literacy often limit understanding. Conversational artificial intelligence systems based on large language models and retrieval-augmented generation may support cannabidiol education, but their safety and reliability remain insufficiently evaluated. This study developed a retrieval-augmented large language model framework that combines structured prompt engineering with curated cannabidiol evidence to generate context-aware guidance for older adults, including those with cognitive impairment. We also proposed an automated, annotation-free evaluation framework to benchmark leading standalone and retrieval-augmented models in the absence of standardized benchmarks. Sixty-four diverse user scenarios were generated by varying symptoms, preferences, cognitive status, demographics, comorbidities, medications, cannabis history, and caregiver support. Multiple state-of-the-art models were evaluated, including a novel ensemble retrieval architecture that integrates multiple retrieval systems. Across three automated evaluation strategies, retrieval-augmented models consistently produced more cautious and guideline-aligned recommendations than standalone models, with the ensemble approach performing best. These findings demonstrate that structured retrieval improves the reliability and safety of AI-driven cannabidiol education and provide a reproducible framework for evaluating AI tools used in sensitive health contexts.
Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI)
Cite as: arXiv:2604.09548 [cs.IR]
  (or arXiv:2604.09548v1 [cs.IR] for this version)
  https://doi.org/10.48550/arXiv.2604.09548

arXiv-issued DOI via DataCite

Submission history

From: Ali Abedi [view email]
[v1] Fri, 16 Jan 2026 04:51:09 UTC (1,092 KB)

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