Understanding Input Masking in LLM Finetuning
Read OriginalThis technical article details the author's exploration of input masking while fine-tuning LLMs with Axolotl for a specific use-case: classifying GitHub pull requests. It explains why masking inputs during training prevents overfitting to prompts, improves generalization, and focuses the model on generating correct outputs rather than memorizing inputs.
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