Welcome to the Healthcare Language Model Evaluation project! This project is dedicated to advancing the field of natural language processing within the healthcare domain. Our primary goal is to provide a robust framework for the automatic evaluation of Large Language Models (LLMs) in healthcare-related tasks.
In this project, we focus on two key experiments:
We are investigating the capabilities of Large Language Models to generate coherent and contextually relevant responses within the healthcare domain. By understanding how LLMs respond to various medical and healthcare-related prompts, we aim to enhance their performance and usefulness in real-world healthcare applications.
Prompt engineering is a crucial aspect of harnessing LLMs for specific tasks. In this experiment, we explore techniques to design effective prompts that lead to desirable responses from the LLMs. By fine-tuning prompts, we aim to harness the full potential of these language models for healthcare applications.
Our ultimate goal is to harness the power of LLMs to not only generate accurate and informative healthcare-related content but also to evaluate, monitor, and improve their performance iteratively. We believe that by enhancing the ability of LLMs to assess and judge other LLMs, we can contribute to the development of more trustworthy and accurate healthcare applications powered by natural language understanding.
To get started with this project, please refer to the documentation and code provided in this repository. You will find resources, examples, and instructions to replicate our experiments and contribute to our mission of advancing LLMs in healthcare.
If you have any questions, feedback, or need assistance, feel free to reach out to our team at aunell@stanford.edu.
Thank you for your interest in the Healthcare Language Model Evaluation project. Together, we can make significant strides in the healthcare natural language processing domain.