Efficient Layer-wise LLM Fine-tuning for Revision Intention Prediction (2025)

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.

## introduction revision is regarded as an important part of writing because it commonly improves the final written work @xcite ; however, as complexity in the process of revision increases, it becomes more difficult to interpret whether revision is in line with the writer's actual intentions @xcite . particularly, determining the intentions between nuanced revisions is challenging for computational models. for example, figure 1 shows the same piece of text revised with different intentions, of which one added a word furthermore to improve coherence, and the other modified a word from style to visual to enhance clarity. prior work @xcite has ## method we propose a novel plug-and-play layer-wise peft framework, i.e., ir-tuning, which attempts to update the weights of important llm layers while keeping those of redundant layers unchanged. figure 3 shows the framework components. ## conclusion we present a layer-wise peft framework named ir-tuning, which dynamically selects a subset of llm layers for fine-tuning while freezing the others. we demonstrate that ir-tuning is effective across multiple revision corpora and llms, and achieves fast fine-tuning convergence and consumes low gpu memory. although ir-tuning is primarily used for text revision in this study, it can potentially be used for other nlp tasks. in future work, we will evaluate its performance on more revision tasks to generalize our findings.