噪声你的提示：在连续扩散语言模型中对条件提示词元加噪
Noise Your Prompt: Noising Conditioning Tokens in Continuous Diffusion Language Models
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中文总结 AI 辅助
本研究通过简单修改连续扩散语言模型的训练目标，对条件提示词元加噪，显著提升组合推理任务（如数独和N皇后）的泛化性能与生成多样性，且无需额外推理成本。
中文摘要 AI 辅助
我们重新审视了连续扩散语言模型文献中一个被标准接受的做法，即在训练期间保持条件提示词元干净。我们做了一个非常简单的修改：在训练期间也对条件提示词元加噪。我们证明，在这种修改后的训练目标下，我们在组合推理任务（如数独和N皇后问题）中实现了更好的泛化，在更难的变体上收益最大（数独困难版求解率从3.73%提升到24.65%），并且生成解决方案的多样性增加（10x10 N皇后问题覆盖率从50.60%提升到73.79%）。我们还展示了在中等数据集规模下，使用Gigaword摘要任务对自然语言生成质量有可测量的改进，但值得注意的是，这些收益并不能迁移到所有自然语言任务（例如开放式对话生成）。我们的方法是对训练目标的一行代码修改，默认情况下不需要额外的推理成本，并提供了受无分类器引导启发的引导采样的灵活性。我们的代码公开可用。
英文摘要
We revisit a standard accepted practice in the continuous diffusion language model literature of fixing conditioning prompt tokens clean during training. We make a very simple modification: also noise the conditioning prompt tokens during training. We demonstrate that under this modified training objective, we achieve better generalization in combinatorial reasoning tasks such as Sudoku and N-Queens, with the largest gains on harder variants ($3.73\% \to 24.65\%$ solve rate on Sudoku Hard), and increased diversity of generated solutions ($50.60\% \to 73.79\%$ coverage on 10x10 N-Queens). We also show measurable improvements to natural language generation quality in modest dataset regimes with Gigaword summarization, but notably demonstrate that gains do not transfer to all natural language tasks (e.g open ended dialogue generation). Our method is a single line change to the training objective, requires no additional inference costs by default, and provides the flexibility of classifier-free guidance inspired guided sampling. Our \href{https://github.com/LateralIntelligence/noise-your-prompt}{code} is publicly available.
发表机构
- Lateral Intelligence
机构由 AI 辅助整理，请以论文原文为准。
