Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes
Intent classification in Large Language Models (LLMs) involves categorizing user prompts into predefined classes. For instance, given a user prompt, the system must determine whether it primarily concerns mathematics, coding, or general text processing. Such classification enables routing prompts to specialized models optimized for specific domains, improving both accuracy and computational efficiency. In this work, we conduct a systematic study comparing training-free vs training-based approaches for intent classification. For this purpose, we consider two lightweight, training-free methods based on statistics of internal representations and compare them against MLP classifiers and linea...
| Authors | Nan Chen, Zhouhao Yang, Soufiane Hayou |
|---|---|
| Primary category | cs.CL |
| Published | 3 August 2026 |
| Last updated at source | 3 August 2026 |
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