Manhin Poon

Online Multi-LLM Selection via Contextual Bandits Under Unstructured Context Evolution

Large language models (LLMs) exhibit diverse response behaviors, costs, and strengths, making it challenging to select the most suitable LLM for a given user query. We study the problem of adaptive multi-LLM selection in an online setting, where the learner interacts with users through multi-step qu