Semi-parametric Prompt-Generation for Model Editing

Harshavardhan Kamarthi, Yen-Chang Hsu, Yilin Shen, Hongxia Jin

Keywords: Model Editing, Prefix Tuning, Fine-tuning

TL;DR: An efficient prefix generation method to efficiently update knowledge of large language models.

Abstract:

Large Language models are used in various downstream tasks with great success. However, changing specific knowledge or beliefs of a model (a.k.a. model editing) efficiently to revise inaccurate predictions while not affecting all other cases is still challenging. Most previous methods compute gradients to change the model. These strategies generally work, paying the cost of high computing and memory complexity. The semi-parametric strategy has recently shown its effectiveness in alleviating the complexity via introducing memory to store the edits of knowledge. However, the memory does not have a proper mechanism to be utilized by a large pre-trained language model, limiting its generalizability to more complicated model editing scenarios. This work proposes a prompt generation mechanism to bridge
the gap. Our method encodes the edits as prefix prompts for language models, then has the large pre-trained language model perform inference with the prompts. In other words, the model is edited by prompts without changing model parameters. Our method, SEPROG, significantly outperforms state-of-art methods by up to 20% on entailed edit benchmarks and provides up to 30% better performance over gradient-based methods on non-entailed benchmarks. These advantages are achieved with much less computation and memory consumption, proving prompt generation’s great potential in model editing problems.

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