Harnessing the Power of Multiple Minds: Lessons Learned from LLM Routing
This addresses the challenge of optimizing LLM usage for reasoning tasks, but it is incremental as it highlights limitations and calls for more robust approaches.
The paper tackled the problem of efficiently directing input queries to the most suitable LLM for reasoning tasks, finding that routing shows promise but is not feasible in all scenarios.
With the rapid development of LLMs, it is natural to ask how to harness their capabilities efficiently. In this paper, we explore whether it is feasible to direct each input query to a single most suitable LLM. To this end, we propose LLM routing for challenging reasoning tasks. Our extensive experiments suggest that such routing shows promise but is not feasible in all scenarios, so more robust approaches should be investigated to fill this gap.