让大模型对答案顺序不敏感，提升推理一致性。
Order Independence With Finetuning
- 用微调将集合式提示融入训练，消除位置偏差。
- 在MMLU等任务上准确率提升，且对顺序变化更鲁棒。
- 适合追求公平、稳定推理结果的模型应用者。
大型语言模型在多项NLP任务中表现优异，但常表现出顺序依赖性：仅重新排列语义相同的标记（如多选题的答案选项）就可能导致预测不一致。近期研究提出集合式提示（SBP）以消除指定标记子集的位置信息，从而缓解位置偏见。然而，在基础模型上应用SBP会引入分布外输入格式，损害分布内性能。本文提出一种微调策略，将SBP整合至训练过程，使这些集合格式的提示更贴近模型训练流形。实验表明，通过微调可有效将SBP融入模型。在分布内（MMLU）和分布外（CSQA、ARC Challenge）的多选任务上，SBP微调显著提升了准确率与对答案顺序变换的鲁棒性，同时保持了整体语言建模能力。本文还讨论了无序建模的广泛影响，并提出了构建更公平、更一致的大模型的未来方向。
原文摘要 · Abstract (English)
Large language models (LLMs) demonstrate remarkable performance on many NLP tasks, yet often exhibit order dependence: simply reordering semantically identical tokens (e.g., answer choices in multiple-choice questions) can lead to inconsistent predictions. Recent work proposes Set-Based Prompting (SBP) as a way to remove order information from designated token subsets, thereby mitigating positional biases. However, applying SBP on base models induces an out-of-distribution input format, which can degrade in-distribution performance. We introduce a fine-tuning strategy that integrates SBP into the training process, "pulling" these set-formatted prompts closer to the model's training manifold. We show that SBP can be incorporated into a model via fine-tuning. Our experiments on in-distribution (MMLU) and out-of-distribution (CSQA, ARC Challenge) multiple-choice tasks show that SBP fine-tuning significantly improves accuracy and robustness to answer-order permutations, all while preserving broader language modeling capabilities. We discuss the broader implications of order-invariant modeling and outline future directions for building fairer, more consistent LLMs.
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