让大模型单次推理完成精准分类，无需重新训练。
SALSA: Single-pass Autoregressive LLM Structured Classification
- 给每个类别分配唯一输出标记，用提示词引导模型输出单标记。
- 在多个基准测试上达到当前最优，且推理效率高。
- 适合需要快速部署、无需微调的文本分类场景。
尽管指令微调的大语言模型具备出色的泛化能力，但在文本分类基准测试中表现仍不佳。本文提出SALSA，一个结合结构化提示、类别到标记映射和参数高效微调的连贯流程，避免冷启动训练。每个类别标签被映射为唯一的输出标记，提示词设计为诱导模型输出单标记响应。推理时，仅将模型输出投影到相关类别标记的逻辑值上，实现单次前向传播下的高效准确分类。SALSA在多个多样化基准测试中取得领先性能，证明其在大模型文本分类应用中的鲁棒性与可扩展性。
原文摘要 · Abstract (English)
Despite their impressive generalization capabilities, instruction-tuned Large Language Models often underperform on text classification benchmarks. We introduce SALSA, a coherent pipeline that combines structured prompting, class-to-token mapping, and parameter-efficient fine-tuning, thereby avoiding cold-start training. Each class label is mapped to a distinct output token, and prompts are constructed to elicit a single-token response. During inference, the model's output is projected only onto the logits of the relevant class tokens, enabling efficient and accurate classification in a single forward pass. SALSA achieves state-of-the-art results across diverse benchmarks, demonstrating its robustness and scalability for LLM-based classification applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。