DiaLLM: חקירה בנושא הפער בין רב-עצמאות וייצור בתרגום אנגלית דיאלקטי

תקציר מקורי באנגליתarXiv:2607.07669v3 Announce Type: replace-cross Abstract: Large language models increasingly understand dialectal English, yet still produce only standard, US-leaning English, leaving dialectal generation, the harder half of the problem, largely unaddressed. We introduce 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 a robustness-generation gap: benchmarks are shaped by continual pretraining and SFT, while alignment visibly reshapes generation in ways benchmarks do not capture.