Model Editing with Canonical Examples

• URL: http://arxiv.org/abs/2402.06155v1
• Date: Fri, 9 Feb 2024 03:08:12 GMT
• Title: Model Editing with Canonical Examples
• Authors: John Hewitt, Sarah Chen, Lanruo Lora Xie, Edward Adams, Percy Liang, Christopher D. Manning
• Abstract summary: We introduce model editing with canonical examples. A canonical example is a simple instance of good behavior, e.g., The capital of Mauritius is Port Louis. We propose sense finetuning, which selects and finetunes a few sense vectors for each canonical example.
• Score: 75.33218320106585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce model editing with canonical examples, a setting in which (1) a single learning example is provided per desired behavior, (2) evaluation is performed exclusively out-of-distribution, and (3) deviation from an initial model is strictly limited. A canonical example is a simple instance of good behavior, e.g., The capital of Mauritius is Port Louis) or bad behavior, e.g., An aspect of researchers is coldhearted). The evaluation set contains more complex examples of each behavior (like a paragraph in which the capital of Mauritius is called for.) We create three datasets and modify three more for model editing with canonical examples, covering knowledge-intensive improvements, social bias mitigation, and syntactic edge cases. In our experiments on Pythia language models, we find that LoRA outperforms full finetuning and MEMIT. We then turn to the Backpack language model architecture because it is intended to enable targeted improvement. The Backpack defines a large bank of sense vectors--a decomposition of the different uses of each word--which are weighted and summed to form the output logits of the model. We propose sense finetuning, which selects and finetunes a few ($\approx$ 10) sense vectors for each canonical example, and find that it outperforms other finetuning methods, e.g., 4.8% improvement vs 0.3%. Finally, we improve GPT-J-6B by an inference-time ensemble with just the changes from sense finetuning of a 35x smaller Backpack, in one setting outperforming editing GPT-J itself (4.1% vs 1.0%).

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