Can Language Feedback Revolutionize AI Training? This Paper Introduces Contrastive Unlikelihood Training (CUT) Framework for Enhanced LLM Alignment

Language models, particularly large ones, have become ubiquitous in AI applications, raising the need for models that align with human values and intentions. Traditionally, alignment has been approached through methods like learning from demonstrations, where human responses guide model fine-tuning, and learning from feedback, using scalar rewards to indicate the desirability of model outputs. However, […]

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