Engineering value of greater model complexity

Determine whether increasing the complexity of pre-trained large language models necessarily produces better engineering outcomes in nanophotonic design, or whether the added complexity is justified by the simpler and more accessible user experience it provides.

Background

The review contrasts conventional task-specific transformer and deep-neural-network workflows with pre-trained LLMs. Greater model complexity may provide broader reasoning capabilities, natural-language interfaces, and reduced dependence on specialized architecture design and training expertise. However, these benefits may come with increased computational, memory, and infrastructure costs. The authors therefore explicitly raise the unresolved question of whether the engineering gains from more complex models outweigh—or are justified by—their usability advantages for nanophotonic design.