OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models

summary

Video file (mp4)

The gist

OpenMedLM presents a novel prompting platform designed to achieve state-of-the-art (SOTA) performance for open-source (OS) large language models in medical question answering, demonstrating that

In short

The episode analyzes the OpenMedLM paper, which demonstrates that prompt engineering can achieve state-of-the-art performance in medical question answering using open-source large language models. Experts conclude that this method democratizes access to advanced AI by proving resource efficiency can surpass specialized tuning efforts.

Key concepts

Prompt Engineering
This technique involves structuring inputs and providing context to a large language model (LLM) to guide its output. The system uses methods like Chain-of-Thought (CoT) to build reasoning step by step, mimicking structured human expertise without needing model retraining.
Fine-Tuning
This is the process of heavily customizing a general AI model using massive amounts of specialized data. The paper suggests that prompt engineering can achieve high performance in medical tasks without requiring this resource-intensive and time-consuming method.
Open-Source Large Language Models (LLMs)
These are foundational models whose weights are publicly available, meaning they are not limited to proprietary systems. This accessibility allows developers to build custom medical tools without being restricted by massive corporate budgets.

Terminology used across episodes

This episode discusses

The paper

OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models · Read on arXiv

Montera, Inc dba Forta

LLMs have become increasingly capable at accomplishing a range of specialized-tasks and can be utilized to expand equitable access to medical knowledge. Most medical LLMs have involved extensive fine-tuning, leveraging specialized medical data and significant, thus costly, amounts of computational power. Many of the top performing LLMs are proprietary and their access is limited to very few research groups. However, open-source (OS) models represent a key area of growth for medical LLMs due to significant improvements in performance and an inherent ability to provide the transparency and compliance required in healthcare. We present OpenMedLM, a prompting platform which delivers state-of-the-art (SOTA) performance for OS LLMs on medical benchmarks. We evaluated a range of OS foundation LLMs (7B-70B) on four medical benchmarks (MedQA, MedMCQA, PubMedQA, MMLU medical-subset). We employed a series of prompting strategies, including zero-shot, few-shot, chain-of-thought (random selection and kNN selection), and ensemble/self-consistency voting. We found that OpenMedLM delivers OS SOTA results on three common medical LLM benchmarks, surpassing the previous best performing OS models that leveraged computationally costly extensive fine-tuning. The model delivers a 72.6% accuracy on the MedQA benchmark, outperforming the previous SOTA by 2.4%, and achieves 81.7% accuracy on the MMLU medical-subset, establishing itself as the first OS LLM to surpass 80% accuracy on this benchmark. Our results highlight medical-specific emergent properties in OS LLMs which have not yet been documented to date elsewhere, and showcase the benefits of further leveraging prompt engineering to improve the performance of accessible LLMs for medical applications.

DOI: 10.1038/s41598-024-64827-6

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models".

Jane: The paper was written by Jenish Maharjana, Anurag Garikipatia, Navan Preet Singha, Leo Cyrusa, Mayank Sharmaa et al. from Montera, Inc dba Forta.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Jane: We also have Lu with us today — senior AI researcher at Tsinghua.

Tom: We also have Meng with us today — lead engineer at a mysterious AI startup.

Jane: We also have Lalam with us today — the in-house Large Language Model.

Tom: Alright, let's get started.

Title: Tom: We are looking at this paper titled "OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models," and it sets a huge topic for discussion right from the start.

Jane: It’s great to see the names of one.AI and all those researchers listed, but it really tells us that we’re looking at a collective effort to challenge current boundaries in healthcare AI.

Lu: The fact that this title points to open-source models is a massive deal, suggesting we aren're not limited to proprietary systems like GPT-four or PaLM.

Meng: I’m interested in the practical implications of this approach; if the model is open source, it means implementation costs can drop significantly for developing custom medical tools.

Lalam: It implies a future where the most advanced medical knowledge isn't gated by massive corporate budgets, which changes the entire landscape of who can access quality AI assistance.

Tom: The initial results in this paper are quite impressive, particularly when you look at how they tested Yi 34B against those four key benchmarks: MedQA, MedMCQA, PubMedQA, and the MMLU medical subset.

Jane: These benchmarks represent incredibly complex tests for how well the AI truly understands clinical knowledge across different domains.

Lu: The overall findings show a clear trend toward superior performance in these open-source models compared to previous records that required heavy fine-tuning.

Meng: They aren't just making marginal gains; they are maximizing the performance of existing, accessible tools by pushing the limits of what they can do right out of the box.

Lalam: It’s about unlocking latent knowledge that was already present in these foundation models and making it useful through a smart prompting strategy.

Tom: The results are quite striking, especially seeing that seventy-two point six percent accuracy on MedQA is a big deal, but how did they achieve this without the heavy fine-tuning?

Jane: They found that OpenMedLM was achieving state-of-the-art performance for open-source models, which is a huge leap forward for the accessibility of these tools.

Lu: It proves that even generalist models have deep, complex knowledge that we can just "prompt" out when we know how to ask the right questions.

Meng: It’s a practical demonstration that resource efficiency and high performance aren't competing concepts in this domain.

Lalam: This is about making sure the power of medical knowledge isn't locked behind proprietary walls or massive computational budgets, democratizing access for everyone who needs it.

Summary: Tom: We’ve seen the impressive results from the open-source models in "OpenMedLM: Prompt engineering can out-perform fine-tuning in medical question-answering with open-source large language models," but how did they manage to achieve such high scores?

Jane: The paper dives deep into the specific components of OpenMedLM, which are really what make this experiment unique compared to just using a simple prompt.

Lu: I love how they're combining techniques like zero-shot and CoT (Chain-of-Thought) to build up the reasoning step by step, mimicking human expertise.

Meng: It shows how you can inject specific, high-quality context into a massive model without having to retrain millions of parameters which would take weeks.

Lalam: This creates a culture where the AI isn't just guessing; it's actively thinking through the possibilities based on the structure we provide it.

Tom: And then, as they build up those prompts, they show us how each component adds value in an ablation study to prove which is most effective.

Jane: The way they combine CoT with kNN selection is particularly clever; it's finding relevant examples from training data to ground the reasoning process.

Lu: I think that’s what makes the whole system feel robust, mimicking a structured thought process rather than just relying on random chance.

Meng: It provides a clear pathway for optimization, letting us see exactly which components contribute most to performance gains in a predictable way.

Lalam: This is about creating an active collaborator with the AI, using its knowledge base in a highly structured way that promotes deeper understanding of the subject matter.

Improvements: Tom: We've seen the mechanics of OpenMedLM and its impressive results, but what does this all mean for the real world? The paper suggests that prompt engineering can achieve SOTA performance on these benchmarks without needing to become a specialized model itself.

Jane: This is a huge departure from the idea that specialized knowledge always requires expensive, dedicated models and massive computational power.

Lu: It truly shows that the inherent potential of generalist AI models, when guided correctly by one.AI's team, is extremely powerful enough to surpass specialized tuning efforts.

Meng: We have to be careful though; while this is fantastic for academic and multiple-choice tasks like MedQA, real clinical scenarios are much messier than a standardized test question.

Lalam: The implication is that we can build more accessible tools for doctors and patients who rely on open-source infrastructure, making AI less of an exclusive luxury.

Tom: It’s clear the future isn't solely about building bigger models; it's about better instructing and guiding the ones we already have available.

Jane: And while the paper acknowledges limitations—like needing to handle complex, open-ended clinical scenarios—it still offers a very promising path forward for continuous improvement.

Lu: We're seeing that the future isn't just about scale, but about how much better instructing and guiding existing models is in a new way of thinking.

Meng: I think we need to keep refining the prompt engineering techniques so that the practical deployment moves beyond these academic benchmarks into real-world application.

Lalam: We should be excited to start using OpenMedLM as a foundational starting point for developing AI tools that support global health initiatives and patient care worldwide.

The Wrap-up: Tom: It's clear we have a lot of exciting avenues for future research here that are worth watching closely, especially since the prompt engineering approach is so effective.

Jane: We’ve learned that OpenMedLM provides a way for open-source AI to achieve strong performance on medical tasks through smart prompting, which is a huge win for accessibility.

Lu: It truly shows that emergent abilities are present in these models when we guide them properly, proving the power of prompt engineering is immense in this field.

Meng: We need to keep researching how to make this practical, ensuring that the implementation is robust and can handle real-world complexities outside these benchmarks.

Lalam: I hope this opens the door for more accessible AI tools that will truly benefit patients worldwide by empowering them with knowledge through smart design.

Tom: The entire team agrees that we’ve seen a massive leap forward in how we approach these medical tasks today.

Lu: I think the authors have really delivered on their promise to showcase how prompt engineering can outperform fine-tuning with open-source large language models.

Meng: It definitely sets a high bar for practical application of resource-efficient AI that I want to see achieved in the coming years.

Lalam: For Lalam, this is about using accessible technology to empower healthcare workers globally and improving health equity for everyone.

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