Artificial Intelligence (AI) has been making remarkable strides in various domains, and one of the most profound challenges has been to develop AI capable of providing medical insights. The Lavita AI team is venturing into this realm. In his recent presentation at Thetacon 23 in Austin TX, Pedram shared Lavita’s plan for improving the performance of the medical Large Language Models (Link to presentation file). What sets this initiative apart is its reliance on the active participation of both experts and non-experts within the community, who play a vital role in refining and enhancing the model through their invaluable feedback.
The Foundation: A Medical LLM
Lavita AI’s journey commences with the development of a specialized Large Language Model (LLM) tailored for medical question answering. This LLM is designed to provide answers to a wide array of medical queries, making it an invaluable resource for anyone seeking reliable healthcare information.
Next Phase: Lavita — Theta Community Engagement
The next step in this journey distinguishes Lavita AI, and its commitment to involving the community in the model’s evolution. This community engagement unfolds on two fronts:
- Harnessing Expertise: Lavita AI actively seeks feedback from medical professionals and domain experts. Their specialized knowledge and expertise are instrumental in assessing the accuracy and safety of the medical responses generated by the LLM at scale. This collaborative approach ensures that the model’s output aligns with the highest medical standards.
- Inclusive Participation: Lavita AI recognizes that the model’s reach extends far beyond the medical community. Anyone seeking health-related information can benefit from the AI’s capabilities. Non-expert users provide a unique perspective, ensuring that the model’s responses are not only accurate but also helpful to the general user population.
RLHF (Reinforcement Learning from Human Feedback) — The Key to Excellence:
One of the methods at the core of Lavita AI’s approach is leveraging RLHF (Reinforcement Learning from Human Feedback). In RLHF, an agent (large language model) can be improved based on user interactions and feedback. During this RLHF process:
- A base LLM will be instruction tuned on medical question answering.
- The trained LLM or a collection of trained LLMs generate responses to a myriad of medical questions.
- These responses undergo evaluation on different axes including safety and helpfulness by both experts and non-expert users to create a preference dataset. This dataset will be used to train a reward model.
- Rewards will be given by the reward model to responses that exhibit accuracy, safety, and helpfulness.
- Penalties are applied to responses that contain false information or fail to address the user’s query adequately.
This iterative feedback-loop mechanism ensures that the LLM is continuously learning, adapting/aligning with human preferences, and improving to provide the best possible answers to medical questions.
Conclusion: Building Medical LLMs Requires Trust and Community Collaboration
Finally, as the model evolves through iterative learning cycles, it inches closer to achieving a level of medical professionals’ performance and aligning with desired safety and helpfulness measures. Lavita AI’s unwavering commitment to quality, accuracy, and safety shines through this diligent approach.
In conclusion, Lavita AI’s journey to build a medical LLM is a testament to the power of community collaboration. By harnessing the collective expertise and insights of both experts and non-experts, Lavita AI is revolutionizing medical question answering. In this quest for excellence, Lavita AI isn’t merely constructing a medical AI; it’s building trust and accuracy in the world of healthcare information.
The future looks promising, and Lavita AI invites you to be part of this transformative journey — a journey towards a smarter, safer, and more accessible medical AI, all powered by the collective wisdom of the community.
