OpenMedLM: Prompt Engineering for Medical Question-Answering with OS LLMs

Conceitos essenciais

OpenMedLM showcases the effectiveness of prompt engineering in achieving SOTA results on medical benchmarks using generalist OS foundation LLMs without extensive fine-tuning.

Resumo

OpenMedLM introduces a prompting platform that leverages robust prompt engineering techniques to enhance the performance of open-source large language models (LLMs) in medical question-answering tasks. By employing various prompting strategies, OpenMedLM surpasses previous best-performing models without the need for computationally costly fine-tuning. The study highlights the potential of generalist OS foundation models to excel in specialized medical tasks and emphasizes the importance of prompt engineering in optimizing model performance.
Key points:

Introduction of OpenMedLM, a prompting platform for medical question-answering with OS LLMs.
Evaluation of different prompting strategies, including zero-shot, few-shot, chain-of-thought, and ensemble voting.
Achieving state-of-the-art results on three common medical LLM benchmarks without extensive fine-tuning.
Comparison with proprietary specialized models and demonstration of superior performance with prompt engineering techniques.
Implications for expanding equitable access to medical knowledge through accessible and transparent LLMs.

Estatísticas

OpenMedLM delivers 72.6% accuracy on the MedQA benchmark, outperforming previous SOTA by 2.4%.
OpenMedLM achieves 81.7% accuracy on the MMLU medical-subset benchmark.

Citações

"OpenMedLM showcases how prompt engineering can optimize performance of generalist OS foundation models in medical applications."
"By leveraging various prompting strategies, OpenMedLM demonstrates the potential of accessible LLMs for healthcare tasks."

Principais Insights Extraídos De

OpenMedLM

by Jenish Mahar... às arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.19371.pdf

Perguntas Mais Profundas

How can prompt engineering be further optimized to enhance the capabilities of generalist OS foundation models?

Prompt engineering can be further optimized by exploring different types of prompts and strategies to elicit more accurate and contextually appropriate responses from generalist OS foundation models. Some ways to enhance prompt engineering include:

Customized Instructions: Tailoring instructions for specific tasks or datasets can help guide the model towards generating more relevant answers.
Multi-step Prompts: Incorporating multi-step prompts, such as chain-of-thought (CoT) prompting, where intermediate steps of reasoning are included in the prompt, can improve the model's ability to generate complex responses.
Fine-tuning Prompt Templates: Experimenting with different prompt templates and structures to find the most effective format for guiding the model towards desired outputs.
Ensemble Techniques: Implementing ensemble techniques like self-consistency voting, where multiple runs of a prompt are averaged or majority-voted, can increase confidence in generated responses.

By continuously experimenting with various prompting techniques and strategies while considering task-specific requirements, researchers can optimize prompt engineering to unlock even greater capabilities in generalist OS foundation models.

How might advancements in prompt engineering impact other industries beyond healthcare?

Advancements in prompt engineering have far-reaching implications beyond healthcare and can revolutionize various industries by enhancing AI capabilities in natural language processing tasks. Some potential impacts include:

Customer Service: Improved natural language understanding through advanced prompting techniques could enhance chatbots' abilities to provide personalized customer service interactions across industries like e-commerce, banking, and telecommunications.

Education: In educational settings, tailored prompts could assist students with personalized learning experiences by providing targeted feedback on assignments or facilitating interactive learning activities based on individual needs.

Legal Services: Advanced prompting strategies could streamline legal research processes by enabling faster retrieval of case law information or assisting lawyers with drafting legal documents efficiently.

Finance: Enhanced natural language processing through optimized prompts could improve financial analysis tools' accuracy and efficiency when analyzing market trends or making investment recommendations.

Overall, advancements in prompt engineering have the potential to transform how AI systems interact with users across diverse sectors by enabling more precise communication and decision-making processes based on contextual cues provided within prompts.

What are the ethical considerations surrounding the use of large language models in healthcare applications?

The use of large language models (LLMs) in healthcare applications raises several ethical considerations that need careful attention:

Data Privacy: LLMs require access to vast amounts of data which may contain sensitive patient information; ensuring robust data privacy measures is crucial to protect patient confidentiality.

Bias Mitigation: LLMs trained on biased datasets may perpetuate existing biases within healthcare systems; efforts must be made to identify and mitigate bias during training phases.

Transparency & Accountability: Understanding how LLMs arrive at their decisions is essential for accountability; transparent reporting mechanisms should be implemented so that clinicians understand why certain recommendations are made.

4 .Informed Consent & Autonomy: Patients should be informed about AI involvement in their care decisions; respecting patient autonomy means involving them actively throughout any AI-assisted treatment process.
5 .**Regulatory Compliance: Ensuring compliance with regulatory standards such as HIPAA is critical when using LLMs for medical purposes; adherence helps maintain trust between patients/providers.
Addressing these ethical considerations requires collaboration among stakeholders including developers,researchers,policymakers,and clinicians,to ensure responsible deploymentofLMMsinhealthcareapplicationswhile prioritizingpatientwell-beingandprivacyconcerns