Recent advancements in Large Language Models (LLMs) have showcased their remarkable capabilities in text understanding and generation. However, even stronger LLMs are susceptible to acquiring erroneous or obsolete information from the training corpus. Direct secondary fine-tuning with data containing new knowledge may be ineffective in updating knowledge due to the conflict between old and new knowledge. In this paper, we propose a new paradigm for fine-tuning called F-Learning (Forgetting before Learning), which employs parametric arithmetic to facilitate the forgetting of old knowledge and learning of new knowledge. Experimental results on two publicly available datasets demonstrate that our proposed F-Learning can obviously improve the knowledge updating performance of both full fine-tuning and LoRA fine-tuning, simultaneously outperforming the existing baselines in most cases. Moreover, we have also discovered that forgetting old knowledge by subtracting the parameters of LoRA can yield a similar effect to subtracting the parameters of full fine-tuning, and occasionally even surpass it significantly.

翻译：近年来，大语言模型（LLMs）在文本理解与生成领域展现出卓越能力。然而，即使是更强大的LLM也容易从训练语料中获取错误或过时信息。由于新旧知识之间存在冲突，直接使用包含新知识的数据进行二次微调可能无法有效更新知识。本文提出一种名为F-Learning（忘记再学习）的新型微调范式，通过参数算术促进旧知识的遗忘与新知识的学习。在两个公开数据集上的实验结果表明，我们提出的F-Learning能显著提升全量微调和LoRA微调的知识更新性能，并在多数情况下优于现有基线方法。此外，我们还发现通过减去LoRA参数遗忘旧知识可产生与减去全量微调参数相似的效果，且有时甚至显著优于后者。