ConstitutionalExperts: Training a Mixture of Principle-based Prompts
Savvas Petridis Ben Wedin Ann Yuan James Wexler Nithum Thain
Large language models (LLMs) are highly capable at a variety of tasks given
the right prompt, but writing one is still a difficult and tedious process. In
this work, we introduce ConstitutionalExperts, a method for learning a prompt
consisting of constitutional principles (i.e. rules), given a training dataset.
Unlike prior methods that optimize the prompt as a single entity, our method
incrementally improves the prompt by surgically editing individual principles.
We also show that we can improve overall performance by learning unique prompts
for different semantic regions of the training data and using a
mixture-of-experts (MoE) architecture to route inputs at inference time. We
compare our method to other state of the art prompt-optimization techniques
across six benchmark datasets. We also investigate whether MoE improves these
other techniques. Our results suggest that ConstitutionalExperts outperforms
other prompt optimization techniques by 10.9% (F1) and that mixture-of-experts
improves all techniques, suggesting its broad applicability.