mlsanityData to 14 Oct 2026

Pareto-Optimized Open-Source LLMs for Healthcare via Context Retrieval

arXiv 2409.15127 ↗First posted 23 Sep 2024cs.AIcited by 2

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Abstract (arXiv)

This study leverages optimized context retrieval to enhance open-source Large Language Models (LLMs) for cost-effective, high performance healthcare AI. We demonstrate that this approach achieves state-of-the-art accuracy on medical question answering at a fraction of the cost of proprietary models, significantly improving the cost-accuracy Pareto frontier on the MedQA benchmark. Key contributions include: (1) OpenMedQA, a novel benchmark revealing a performance gap in open-ended medical QA compared to multiple-choice formats; (2) a practical, reproducible pipeline for context retrieval optimization; and (3) open-source resources (Prompt Engine, CoT/ToT/Thinking databases) to empower healthcare AI development. By advancing retrieval techniques and QA evaluation, we enable more affordable and reliable LLM solutions for healthcare.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 26 references that are in the map. First posted on arXiv 23 Sep 2024.

Builds on

18 of 26 references.

References · 18

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2020Measuring Massive Multitask Language Understandingcited by 7,963
  6. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  7. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,211
  8. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  9. 2022MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answeringcited by 543
  10. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 384
  11. 2023Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicinecited by 329
  12. 2022Can large language models reason about medical questions?cited by 194
6 more
  1. 2023MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrievalcited by 185
  2. 2023AnglE-optimized Text Embeddingscited by 99
  3. 2023Diagnostic Reasoning Prompts Reveal the Potential for Large Language Model Interpretability in Medicinecited by 83
  4. 2024Gemini Goes to Med School: Exploring the Capabilities of Multimodal Large Language Models on Medical Challenge Problems & Hallucinationscited by 27
  5. 2024OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language modelscited by 20
  6. 2024Aloe: A Family of Fine-tuned Open Healthcare LLMscited by 20

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Citing papers per year: 2025: 2.

2 papers.

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