对比零样本与训练式意图分类，发现后者更准但前者更鲁棒。
Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes
- 用模型内部表征统计做零样本分类，不需训练
- 训练式方法在复杂任务（如Java vs Python）上更准
- 零样本方法对混合意图和对抗输入更稳定
大型语言模型中的意图分类旨在将用户提示归入预定义类别，例如判断其主要涉及数学、编程或通用文本处理。此类分类可将提示路由至针对特定领域优化的专用模型，从而提升准确率与计算效率。本文系统比较了零样本与训练式意图分类方法。研究采用两种基于内部表征统计的轻量级零样本方法，并与MLP分类器和线性探测器对比。全面实证表明：1）两类方法在简单任务（数学vs.编程vs.自然语言）上均达饱和；2）训练式分类器在更难任务（如Java vs Python）上表现更优；3）零样本方法对混合意图及对抗性提示普遍更具鲁棒性。
原文摘要 · Abstract (English)
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 linear probes. Our comprehensive empirical evaluation reveals that 1) Both training-free and training-based methods saturate easy benchmarks (mathematics vs. coding vs. natural language), 2) Training-based classifiers have an advantage on harder classification tasks (e.g. Java vs Python), and 3) Training-free methods are generally more robust to mixed-intent and adversarial prompts.
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