Effectiveness in unfamiliar design spaces lacking LLM prior knowledge

Determine the effectiveness of the proposed LLM-guided Bayesian optimization approach—which integrates natural language guidance into multi-objective Bayesian optimization—when applied to unfamiliar design spaces in which the large language model has no prior domain knowledge, thereby assessing its ability to generalize beyond known domains.

Background

The method relies on an LLM to interpret designer requests and select among BO-generated candidates, using predictive means and variances and the history of evaluated configurations. While the authors argue that BO’s statistical signals and the LLM’s in-context learning may enable generalization, they have not empirically validated performance in domains where the LLM lacks prior knowledge.

They explicitly acknowledge that establishing effectiveness under such conditions remains unresolved, motivating future empirical validation on design problems that are novel to the LLM.