SALSA：单遍自回归大语言模型结构化分类
SALSA: Single-pass Autoregressive LLM Structured Classification
AI总结：
SALSA通过结构化提示、类别到令牌映射和参数高效微调，实现单次前向传播的高效准确文本分类，在多个基准上达到最先进水平。
摘要：
尽管指令微调的大语言模型具有令人印象深刻的泛化能力，但它们在文本分类基准上往往表现不佳。我们引入了SALSA，一个连贯的流水线，它结合了结构化提示、类别到令牌映射和参数高效微调，从而避免了冷启动训练。每个类别标签被映射到一个独特的输出令牌，并构建提示以引发单令牌响应。在推理期间，模型的输出仅投影到相关类别令牌的逻辑值上，从而在单次前向传播中实现高效且准确的分类。SALSA在多个不同基准上取得了最先进的结果，展示了其在大语言模型分类应用中的稳健性和可扩展性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
