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News/DrugRAG: Enhancing Pharmacy LLM Performance Through A Novel Retrieval-Augmented Generation Pipeline
arxiv
PublishedMay 22, 2026 at 4:00 AM
▲bullish

DrugRAG: Enhancing Pharmacy LLM Performance Through A Novel Retrieval-Augmented Generation Pipeline

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2512.14896v2 Announce Type: replace-cross Abstract: In our study, we evaluated large language model (LLM) performance on pharmacy licensure-style question-answering tasks and developed an external knowledge integration method to improve accuracy. We benchmarked ten LLMs with varying parameter

Models mentioned
01
  • 01meta-llama logo
    Llama-3.1-8B
    meta-llama/Llama-3.1-8B
    DL 1.6M+0.7%IN $0.10/Mtok
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Discussion
Mentioned models
04
  • 01
    GPT-5
  • 02
    o3
  • 03
    Gemma 3
  • 04
    Llama-3.1-8B
    meta-llama/Llama-3.1-8B
    1.6M dl
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#pharmacy#language models#question answering#knowledge integration

No replies yet. Be first.

Mentioned models
04
  • 01
    GPT-5
  • 02
    o3
  • 03
    Gemma 3
  • 04
    Llama-3.1-8B
    meta-llama/Llama-3.1-8B
    1.6M dl
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#pharmacy#language models#question answering#knowledge integration

Related coverage

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arxivThe Steering Budget: Examples beat Knobs17harxivPolestar: Drift-Aware Cache Calibration and Token Commitment for Efficient Inference of Diffusion LLMs17harxivRxBrain: Embodied Cognition Foundation Model with Joint Language-Visual Reasoning and Imagination17harxivWhen a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models17h
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Originally published on arxiv ↗
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