Saeed Esmaili • 6/29/2024

Understanding Input Masking in LLM Finetuning

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This 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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#Training Data #Model Training #LLM Finetuning
Understanding Input Masking in LLM Finetuning

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