DADA：通过动态聚合语言规则实现方言适应
DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules
AI总结：
针对 LLM 在非标准英语方言上性能下降且方言边界难以离散分类的问题，本文提出模块化框架 DADA，通过动态组合语言特征适配器实现可解释、可扩展的单方言与多方言适应。
摘要：
现有的主要关注标准美国英语（SAE）的大型语言模型（LLM）在应用于其他英语方言时，往往会导致性能显著下降。尽管现有的缓解措施针对个别目标方言处理差异，但它们假设可以使用高精度的方言识别系统。方言之间的边界本质上是灵活的，因此很难将语言归入离散的预定义类别。在本文中，我们提出了 DADA（Dialect Adaptation via Dynamic Aggregation，通过动态聚合实现方言适应），这是一种模块化方法，通过组合处理特定语言特征的适配器，使经过 SAE 训练的模型具备多方言鲁棒性。DADA 的组合架构既允许针对特定方言变体进行定向适应，也允许同时适应各种方言。我们表明，DADA 对单任务模型和指令微调语言模型都有效，为使现有 LLM 适应不同英语方言提供了一个可扩展且可解释的框架。
英文摘要：
Existing large language models (LLMs) that mainly focus on Standard American English (SAE) often lead to significantly worse performance when being applied to other English dialects. While existing mitigations tackle discrepancies for individual target dialects, they assume access to high-accuracy dialect identification systems. The boundaries between dialects are inherently flexible, making it difficult to categorize language into discrete predefined categories. In this paper, we propose DADA (Dialect Adaptation via Dynamic Aggregation), a modular approach to imbue SAE-trained models with multi-dialectal robustness by composing adapters which handle specific linguistic features. The compositional architecture of DADA allows for both targeted adaptation to specific dialect variants and simultaneous adaptation to various dialects. We show that DADA is effective for both single task and instruction finetuned language models, offering an extensible and interpretable framework for adapting existing LLMs to different English dialects.
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。
