Evaluating Escalation Signals for LLM Routing: Targets, Controls, and Five Ways to Fool Yourself
Read the original on arXiv AI →The paper investigates whether semantic entropy—a measure of disagreement among a language model’s sampled answers—can serve as a cheap signal for deciding when to route a query from a small to a larger language model. Experiments on GSM8K and other benchmarks show that semantic entropy can distinguish small‑model mistakes and improve routed accuracy, but the authors also reveal that a simple question‑difficulty rule can mimic its performance and that other factors (definition of success, benchmark design, live sampling cost) can undermine its effectiveness. They propose a checklist of checks to validate escalation signals and demonstrate how to predict when a cached‑outcome approach will fail. "whyItMatters":"The study highlights the importance of rigorous evaluation of escalation signals, showing that seemingly promising metrics can be misleading without proper controls and that practical routing decisions must account for cost and benchmark design."
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