Efficient Knowledge Updates for AI: LLM Surgery

LLM Surgery efficiently updates AI models by removing outdated information, incorporating new knowledge, and maintaining overall performance without full retraining, reducing computational costs.

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schedule Sep 25, 2024
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Large language models (LLMs) have transformed industries from customer service to content creation. However, they come with a challenge: once trained, they can't forget outdated or incorrect information, nor can they easily learn new facts without being retrained entirely.

The issue of outdated or problematic information is significant. Retraining is an expensive process in terms of both time and computational resources.

Enter LLM Surgery, a breakthrough framework designed to modify LLMs by removing old knowledge, integrating new data, and retaining essential information—all without the need for full retraining.

LLM Surgery provides an efficient solution: it selectively "unlearns" outdated knowledge, updates models with fresh information, and ensures that performance on other tasks remains stable.

At the core of LLM Surgery is a three-part system:

  • First, it uses reverse gradient techniques to remove unwanted knowledge.
  • Then, it applies gradient descent to add new, up-to-date information.
  • Finally, it maintains performance consistency by minimizing differences between the original and modified model on unrelated data.

    • Using a Llama2 model, the researchers showed that LLM Surgery improved accuracy by 20% on new data, while successfully erasing outdated information.

    Most impressively, the method reduces the computation time by a factor of 35 compared to traditional retraining approaches. This makes LLM Surgery a scalable and cost-effective solution for AI systems that need to keep up with the ever-changing world.

    article
    Veldanda, A. K., Zhang, S.-X., Das, A., Chakraborty, S., Rawls, S., Sahu, S., & Naphade, M. (2024). LLM Surgery: Efficient Knowledge Unlearning and Editing in Large Language Models. arXiv, 2409.13054. Retrieved from https://arxiv.org/abs/2409.13054v1

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