用多任务提示训练大模型分类器，能提升跨领域泛化能力。
How Useful Is Cross-Domain Generalization for Training LLM Monitors?
- 通过多任务提示训练提升模型在新领域上的分类表现。
- 模型在相近领域表现提升，但在提示完全改变时会失效。
- 混合指令跟随训练可缓解泛化失败，适合构建监控系统。
使用提示式语言模型进行分类可在数据有限的领域中实现分类，但难以获得微调带来的鲁棒性与性能优势。本文研究在多个分类任务（每个任务对应独立提示）上训练是否能提升模型在新领域和新提示下的表现。结果表明，该训练方式可在邻近领域部分泛化，显著提升未见过的任务性能；然而在提示发生根本性变化但数据域不变的情况下，模型会出现严重失效。通过将分类训练与通用指令遵循训练相结合，可保留分类训练的优势并缓解其泛化缺陷。令人意外的是，仅通过无思考的监督分类训练，模型也能泛化至需思考的分类与摘要任务，暗示此类训练在构建其他类型分类器与监控系统中具有实用价值。
原文摘要 · Abstract (English)
Using prompted language models as classifiers enables classification in domains with limited training data, but misses some of the robustness and performance benefits that fine-tuning can bring. We study whether training on multiple classification tasks, each with its own prompt, improves performance on new domains with new classification prompts. We show that such training partially generalizes to adjacent domains, improving classification performance on tasks that are unseen during training. However, we identify specific edge cases where the fine-tuned models fail to follow prompts, such as when the classification prompt changes completely while the data domain remains the same as during training. We show that classification training can be mixed with general instruction following training, and that (when done well) such training keeps the benefits of classification training and mitigates its generalization failures. Surprisingly, we see that this no-thinking supervised classification training can generalize to with-thinking classification and summarization, suggesting that no-thinking classification training might be instrumentally useful in building other kinds of classifiers and monitoring systems.
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