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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 664K-1.2%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
    0.7M 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
    0.7M dl
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
04
#pharmacy#language models#question answering#knowledge integration

Related coverage

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arxivOptimal Hydra: Model Heterogeneous Federated Learning for LLMs using Data-Informed Hydra-Nets1darxivJudging the Judges: Confidence and Disagreement in Rubrics as Rewards1darxivContent associations with answer selection and engagement in supply chain community question answering1d
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