CausalEvo: Causal Skill Self-Evolution for Health LLM Agents
In brief
- CausalEvo automatically selects useful textual skills for healthcare LLM agents using a causal method.
- The approach improves accuracy to 60% on six verified medical tasks, versus 55.83% for a baseline method.
- It reduces token usage by 55.6% compared to an existing approach.
CausalEvo is a method enabling healthcare-focused LLM agents to evolve by selecting the most useful textual skills from task feedback using a causal approach. It boosts accuracy to 60% across six verified medical tasks while cutting token usage by 55.6% compared to a baseline method.
What it changes for you
- HealthHealthcare LLM agents could adopt CausalEvo to enhance the accuracy of medical calculations and reduce generalization errors.
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