Dialectal robustness and generation are dissociated in LLMs: benchmarks are driven by pretraining and SFT while alignment reshapes generation invisibly to benchmarks, and the method maximizing dialectal reward is least preferred by human evaluators.
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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
DLR augments low-rank factorization with a fixed structured residual during training that is absorbed post-training, improving C4 perplexity for LLaMA models from 60M to 7B while preserving exact low-rank inference cost.
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DiaLLM: An Investigation into the Robustness-Generation Gap in English Dialect Adaptation
Dialectal robustness and generation are dissociated in LLMs: benchmarks are driven by pretraining and SFT while alignment reshapes generation invisibly to benchmarks, and the method maximizing dialectal reward is least preferred by human evaluators.
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DLR: Zero-Inference-Cost Latent Residuals for Low-Rank Pre-Training
DLR augments low-rank factorization with a fixed structured residual during training that is absorbed post-training, improving C4 perplexity for LLaMA models from 60M to 7B while preserving exact low-rank inference cost.