DADA: Dialect Adaptation via Dynamic Aggregation of Linguistic Rules

Understand

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.