Efficient Out-of-Scope Detection in Dialogue Systems via Uncertainty-Driven LLM Routing

Out-of-scope (OOS) intent detection is a critical challenge in task-oriented dialogue systems (TODS), as it ensures robustness to unseen and ambiguous queries.In this work, we propose a novel but simple modular framework that combines uncertainty modeling with fine-tuned large language models (LLMs) for efficient and accurate OOS detection.The first step applies uncertainty estimation to the output of an inscope intent detection classifier, which is currently deployed in a real-world TODS handling tens of thousands of user interactions daily.The second step then leverages an emerging LLMbased approach, where a fine-tuned LLM is triggered to make a final decision on instances with high uncertainty.Unlike prior approaches, our method effectively balances computational efficiency and performance, combining traditional approaches with LLMs and yielding state-ofthe-art results on key OOS detection benchmarks, including real-world OOS data acquired from a deployed TODS.