通过推理进行正则化：通过解释增强微调提升语言模型分类性能
Regularization Through Reasoning: Systematic Improvements in Language Model Classification via Explanation-Enhanced Fine-Tuning
AI总结：
通过在微调中加入解释增强，提升语言模型分类的准确性和可靠性，揭示令牌级结构对推理过程的影响。
摘要：
对LLM进行分类微调通常直接将输入映射到标签。我们询问在微调过程中为每个标签附加简短解释是否能获得更好的模型。我们从三个方面评估对话响应质量：自然性、全面性和主题相关性，每个方面均在5分量表上评分。使用多个LLM生成的数据集，我们对一个7B参数模型进行微调，并在六个多样化的对话数据集上测试。在18个数据集、任务设置中，标签加解释的训练优于仅标签的基线。一个核心且意外的结果涉及随机令牌。我们用语法不连贯但词汇与原词一致的文本替代人工写的解释（例如，洗牌或词袋变体）。尽管缺乏语义，这些伪解释仍能提高准确性，且经常缩小到真实解释的差距。效果在不同数据集和训练种子中持续存在，表明收益来自结构而非意义：额外的令牌预算鼓励更丰富的中间计算，并作为减少自信捷径的正则化器。内部分析支持这一观点：解释增强的模型在中间层表现出更高的激活熵，同时在输出层有更尖锐的预测质量，一致表明在决策前有更多推理。总体而言，无论使用真实理由还是精心构造的随机令牌序列，解释增强的微调都能提高LLM分类的准确性和可靠性，并阐明令牌级支架如何在推理期间塑造计算。
英文摘要：
Fine-tuning LLMs for classification typically maps inputs directly to labels. We ask whether attaching brief explanations to each label during fine-tuning yields better models. We evaluate conversational response quality along three axes: naturalness, comprehensiveness, and on-topic adherence, each rated on 5-point scales. Using ensemble-generated data from multiple LLMs, we fine-tune a 7B-parameter model and test across six diverse conversational datasets. Across 18 dataset, task settings, label-plus-explanation training outperforms label-only baselines. A central and unexpected result concerns random tokens. We replace human-written explanations with text that is syntactically incoherent yet vocabulary-aligned with the originals (e.g., shuffled or bag-of-words variants). Despite lacking semantics, these pseudo-explanations still improve accuracy over label-only training and often narrow much of the gap to true explanations. The effect persists across datasets and training seeds, indicating that gains arise less from meaning than from structure: the extra token budget encourages richer intermediate computation and acts as a regularizer that reduces over-confident shortcuts. Internal analyses support this view: explanation-augmented models exhibit higher activation entropy in intermediate layers alongside sharper predictive mass at the output layer, consistent with increased deliberation before decision. Overall, explanation-augmented fine-tuning, whether with genuine rationales or carefully constructed random token sequences, improves accuracy and reliability for LLM classification while clarifying how token-level scaffolding shapes computation during inference.
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。
