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

• URL: http://arxiv.org/abs/2507.01541v1
• Date: Wed, 02 Jul 2025 09:51:41 GMT
• Title: Efficient Out-of-Scope Detection in Dialogue Systems via Uncertainty-Driven LLM Routing
• Authors: Álvaro Zaera, Diana Nicoleta Popa, Ivan Sekulic, Paolo Rosso,
• Abstract summary: Out-of-scope (OOS) intent detection is a critical challenge in task-oriented dialogue systems (TODS)<n>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.
• Score: 6.579756339673344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 in-scope 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 LLM-based 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-of-the-art results on key OOS detection benchmarks, including real-world OOS data acquired from a deployed TODS.

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