这篇论文挖出了LLM理解方言但生成不了方言的深层矛盾，还对比了多种对齐方法，结果很反直觉。
DiaLLM 在 International Corpus of English 上持续预训练三种开源模型家族（LLaMA、Mistral、Gemma），并应用隐式和显式后训练范式各搭配三种对齐策略，首次在澳大利亚、印度和北英格兰方言上对比这些组件。结果显示鲁棒性和生成能力分离：基准测试由持续预训练和 SFT 主导，而对齐显著重塑生成但基准测试无法捕捉。显式方言适配生成被人类评估者可靠识别为方言，但最激进优化方言奖励的方法反而得不到人类偏好。独立语言学分析在两个模型家族上验证了该奖励-质量差距，且没有单一对齐方法占优。
DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation
Large language models increasingly \emph{understand} dialectal English, yet still \emph{produce} only standard, US-leaning English, leaving dialectal generation, the harder half of the problem, largely unaddressed. We introduce \textbf{DiaLLM}, which continually pretrains three open-weight language model families on the International Corpus of English and applies implicit and explicit post-training paradigms, each combined with three model alignment strategies, giving the first controlled comparison of these components across Australian, Indian, and Northern British English. Our results reveal that dialectal robustness and generation are \emph{dissociated}: benchmarks are shaped by continual pretraining and SFT, while alignment visibly reshapes generation in ways benchmarks do not capture. Explicit variety-targeted adaptation produces output reliably recognised as dialectal and preferred over broad alignment, yet the method that most aggressively optimises the dialectal reward is not preferred by human evaluators. Independent linguistic analysis corroborates this reward-quality gap, most clearly on two of the three families. No single alignment method dominates, and closing the gap will require richer reward designs and continued investment in dialectal resources. We release all code, checkpoints, and preference datasets.