Abstract
Large Language Models (LLMs) have shown extraordinary success across various text generation tasks; however, their potential for simple yet essential text classification remains underexplored, as LLM pre-training tends to emphasize generation over classification. While LLMs with instruction tuning can transform classification into a generation task, they often struggle to categorize nuanced texts. One such example is text revision, which involves nuanced edits between pairs of texts. Although simply fine-tuning LLMs for revision classification seems plausible, it requires a large amount of revision annotations, which are exceptionally expensive and scarce in the community. To address this issue, we introduce a plug-and-play layer-wise parameter-efficient fine-tuning (PEFT) framework, i.e., IR-Tuning, which fine-tunes a subset of important LLM layers that are dynamically selected based on their gradient norm distribution, while freezing those of redundant layers. Extensive experiments suggest that IR-Tuning surpasses several layer-wise PEFT baselines over diverse text revisions, while achieving fast convergence, low GPU memory consumption, and effectiveness on small revision corpora.
Abstract (translated)
大型语言模型（LLMs）在各种文本生成任务中展示了非凡的成功；然而，它们对于简单而基本的文本分类的潜力尚未得到充分探索，因为大规模预训练倾向于强调生成而非分类。虽然通过指令微调可以使LLM将分类问题转化为生成任务，但它们常常难以对细微差别较大的文本进行准确分类。其中一个例子就是文本修订，这涉及到两段文本之间的微妙编辑差异。尽管简单地为LLMs的修订分类进行微调似乎是可行的，但这需要大量的修订注释，而这些数据在社区中极其昂贵且稀缺。 为了应对这一问题，我们引入了一种即插即用层级参数高效微调（PEFT）框架，即IR-Tuning。该方法选择性地对一组重要层级进行细调，这些层级是基于它们的梯度范数分布动态选定的，并冻结那些冗余层级。通过广泛的实验表明，IR-Tuning在多种文本修订任务中超越了几种层级PEFT基线模型，同时实现了快速收敛、低GPU内存消耗以及对于小型修订语料库的有效性。
URL
https://arxiv.org/abs/2510.00268
