OpenMedLM: Prompt Engineering for Medical Question-Answering

Keskeiset käsitteet

Generalist OS foundation models can achieve high performance on medical benchmarks through prompt engineering, surpassing specialized fine-tuned models.

Tiivistelmä

OpenMedLM showcases the effectiveness of prompt engineering in achieving state-of-the-art results for open-source large language models (LLMs) on medical benchmarks. By utilizing various prompting strategies, such as zero-shot, few-shot, chain-of-thought, and ensemble/self-consistency voting, OpenMedLM outperforms previous best-performing models that relied on computationally expensive fine-tuning. The study highlights the potential of generalist OS foundation models to excel in specialized medical tasks without the need for extensive fine-tuning. Results demonstrate significant improvements in accuracy across multiple medical benchmarks, showcasing the benefits of prompt engineering techniques in enhancing LLM performance for healthcare applications.

Tilastot

OpenMedLM displays a 72.6% accuracy on the MedQA benchmark.
Achieves 81.7% accuracy on the MMLU medical-subset benchmark.

Lainaukset

"Prompt engineering can outperform fine-tuning in achieving high performance for medical question-answering."
"OpenMedLM delivers state-of-the-art results on three common medical LLM benchmarks."
"Our results highlight emergent properties in OS LLMs specific to healthcare tasks."

Tärkeimmät oivallukset

OpenMedLM

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

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

Syvällisempiä Kysymyksiä

How can prompt engineering be further optimized to enhance the performance of generalist OS foundation models beyond medical applications?

Prompt engineering can be further optimized by exploring a variety of strategies and techniques tailored to specific tasks and datasets. One approach is to experiment with different types of prompts, such as providing more context or examples in the prompt text. This could involve incorporating domain-specific knowledge or using structured templates that guide the model towards generating more accurate responses.
Additionally, researchers can explore advanced prompting strategies like hierarchical prompting, where prompts are organized in a nested structure to guide the model through complex reasoning processes. By breaking down tasks into smaller sub-tasks within the prompt, models can better understand and generate responses for multifaceted questions.
Furthermore, leveraging external knowledge sources or pre-processing data before feeding it into the model through prompts can enhance performance. Techniques like information retrieval-based prompting or utilizing external databases for context enrichment can provide valuable information that aids in generating more informed responses.
Moreover, fine-tuning prompt parameters based on empirical observations and iterative experimentation can help optimize prompt design for specific tasks. Continuous refinement of prompts based on feedback from model outputs and evaluation results is crucial for improving overall performance across various applications beyond just healthcare settings.

How do potential limitations or biases introduced by relying solely on prompt engineering affect model optimization?

Relying solely on prompt engineering for model optimization introduces several potential limitations and biases that need to be carefully considered:

Prompt Bias: The design of prompts may inadvertently introduce bias into the training process, leading to skewed outputs from the model. Biases present in the language used in prompts or implicit assumptions made during prompt creation can impact how models interpret and respond to queries.

Limited Generalization: Models heavily reliant on specific prompts may struggle when faced with unseen data or tasks outside their trained scope. Over-reliance on engineered prompts may hinder a model's ability to generalize effectively across diverse scenarios.

Data Efficiency Concerns: Depending solely on well-crafted prompts may require larger amounts of annotated data compared to other optimization methods like fine-tuning. This could pose challenges in scenarios where labeled data is scarce or expensive to acquire.

Complexity Management: Managing intricate prompting structures across multiple tasks could increase complexity and maintenance overheads over time, potentially impacting scalability and usability of models in real-world applications.

How can insights from prompt engineering research be applied to other fields beyond healthcare...

...to improve model performance?
Insights gained from research in prompt engineering within healthcare settings have broader implications across various domains:

Natural Language Understanding (NLU): Applying sophisticated prompting techniques developed for medical question-answering tasks could enhance NLU capabilities across industries such as customer service chatbots, legal document analysis, sentiment analysis tools, etc., enabling more accurate understanding and generation of natural language text.

2Financial Analysis: Utilizing advanced prompting strategies inspired by medical benchmarks could improve financial modeling accuracy by guiding large language models through complex financial forecasting scenarios with structured inputs tailored specifically for economic predictions.
3Legal Research: Adapting successful approaches from medical Q&A benchmarks could assist legal professionals in conducting comprehensive legal research efficiently by formulating precise queries that extract relevant information from vast repositories of case law documents.
4Academic Research: Leveraging insights from effective health-related promp...