DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation

Researchers have identified a significant gap in large language models' ability to generate dialectal English, despite their improving understanding of dialects. The introduction of DiaLLM aims to address this issue by continually pretraining language models on the International Corpus of English, incorporating implicit and explicit post-training methods. This approach enables the models to better capture the nuances of dialectal English, moving beyond the standard US-leaning English typically produced. The DiaLLM model focuses on three open-weight language model families, leveraging the diversity of the International Corpus of English to enhance dialectal generation capabilities¹. The implications of this research extend beyond language models, as state-aligned threat activity can raise the stakes from criminal to geopolitical. Therefore, advancements in dialectal generation, such as those achieved by DiaLLM, matter to practitioners as they can inform the development of more sophisticated language-based security measures.