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Style-Debiased DPO: Updating LLM Knowledge with Factuality-Aware Synthetic Preference Data
Continued pretraining (CPT) with data augmentation such as paraphrasing can store inside a large language model (LLM) the knowledge of a small source corpus. The stored knowledge, however, is not always retrieved correctly. We study the eliciting side rather than the storing side: we use preference
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