让大模型通过学习连续扰动提升跨域推理能力
Learning Perturbations to Extrapolate Your LLM
- 用可学习的连续向量动态调整提示词前缀
- 在真实数据集上实现显著优于基线的跨域性能
- 适合需要强泛化能力的模型部署场景
近期研究表明，在大语言模型中注入扰动可显著提升其外推性能。然而，现有方法多依赖固定设计的离散扰动，灵活性不足。本文提出一种新框架：在嵌入空间中，通过可学习变换对提示词前缀进行连续扰动。为应对不可计算的边缘似然问题，我们推导出无偏参数估计方程，并通过随机梯度下降进行优化。我们在过参数化条件下建立了估计器的统计性质。在合成与真实数据集上的实证评估表明，该方法在多种主流基线方法之上实现了显著的域外性能提升。
原文摘要 · Abstract (English)
Recent advancements in large language models demonstrate that injecting perturbations can substantially enhance extrapolation performance. However, current approaches often rely on discrete perturbations with fixed designs, which limits their flexibility. In this work, we propose a framework where token prefixes are perturbed by a learnable transformation of a continuous latent vector within an embedding space. To overcome the challenge of an intractable marginal likelihood, we derive unbiased estimating equations for model parameters and optimize them via stochastic gradient descent. We establish the statistical properties of the resulting estimator in over-parameterized regimes. Empirical evaluations on both synthetic and real-world datasets demonstrate that our proposal yields significant gains in out-of-domain settings over a range of state-of-the-art baseline methods.
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