How to Surgically Edit LLMs Without Retraining in Data Science

Author(s): The Bot Group

Originally published on Towards AI.

How to Surgically Edit LLMs Without Retraining in Data Science

Your large language model is a marvel of engineering, trained on vast datasets at an enormous cost. It’s powerful, fluent, and… wrong. It confidently states an outdated fact, misremembers a user’s preference, or reflects a bias you need to correct. The traditional solution? Retraining or fine-tuning, an expensive process that risks “catastrophic forgetting,” where the model loses other valuable knowledge. This is a critical challenge in modern data science.

Model editing represents a paradigm shift in how we manage and maintain AI systems. It offers a path toward models that can be corrected, updated, and personalized with precision and efficiency. Techniques like ROME provide targeted updates while preserving existing knowledge, and rigorous model evaluation is critical to ensure that changes do not lead to catastrophic side effects.

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Published via Towards AI