Unveiling the LoRA Assumption: Why Fine-Tuning May Lead to Wrong Outputs

LoRA, a popular method for fine-tuning large AI models, assumes uniformity in how updates are applied to a model. While effective for modifying styles like tone or format, it struggles with incorporating complex factual knowledge, leading to unstable training and incomplete outputs. This…

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