# Efficient Layer-wise LLM Fine-tuning for Revision Intention Prediction

arXiv: https://arxiv.org/abs/2510.00268 · HTML: https://mlsanity.com/paper/2510.00268/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 59 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [DoRA: Weight-Decomposed Low-Rank Adaptation](https://mlsanity.com/paper/2402.09353/) (2024)
- [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://mlsanity.com/paper/2110.07602/) (2021)
- [LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding](https://mlsanity.com/paper/2404.16710/) (2024)
- [Surgical Fine-Tuning Improves Adaptation to Distribution Shifts](https://mlsanity.com/paper/2210.11466/) (2022)
- [On the Effect of Dropping Layers of Pre-trained Transformer Models](https://mlsanity.com/paper/2004.03844/) (2020)
- [LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning](https://mlsanity.com/paper/2403.17919/) (2024)
- [Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference](https://mlsanity.com/paper/2304.04947/) (2023)
- [RewriteLM: An Instruction-Tuned Large Language Model for Text Rewriting](https://mlsanity.com/paper/2305.15685/) (2023)
- [Understanding Iterative Revision from Human-Written Text](https://mlsanity.com/paper/2203.03802/) (2022)
- [Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision](https://mlsanity.com/paper/2204.03685/) (2022)
- [LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation](https://mlsanity.com/paper/2402.07721/) (2024)
- [ARIES: A Corpus of Scientific Paper Edits Made in Response to Peer Reviews](https://mlsanity.com/paper/2306.12587/) (2023)
- [arXivEdits: Understanding the Human Revision Process in Scientific Writing](https://mlsanity.com/paper/2210.15067/) (2022)
- [Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond](https://mlsanity.com/paper/2205.11484/) (2022)
- [NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge](https://mlsanity.com/paper/2206.07106/) (2022)
- [ACLSum: A New Dataset for Aspect-based Summarization of Scientific Publications](https://mlsanity.com/paper/2403.05303/) (2024)
- [CASIMIR: A Corpus of Scientific Articles enhanced with Multiple Author-Integrated Revisions](https://mlsanity.com/paper/2403.00241/) (2024)
- [Flexora: Flexible Low Rank Adaptation for Large Language Models](https://mlsanity.com/paper/2408.10774/) (2024)
- [Annotation and Classification of Sentence-level Revision Improvement](https://mlsanity.com/paper/1909.05309/) (2019)
- [Re3: A Holistic Framework and Dataset for Modeling Collaborative Document Revision](https://mlsanity.com/paper/2406.00197/) (2024)
- [Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions](https://mlsanity.com/paper/2410.02028/) (2024)
- [Annotation and Classification of Evidence and Reasoning Revisions in Argumentative Writing](https://mlsanity.com/paper/2107.06990/) (2021)
- [To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support](https://mlsanity.com/paper/2305.16799/) (2023)

## Built on by (most cited first, 3 of 3)

- [Understanding and Guiding Layer Placement in Parameter-Efficient Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2602.04019/) (2026)
- [Beyond Temperature: Hyperfitting as a Late-Stage Geometric Expansion](https://mlsanity.com/paper/2605.22579/) (2026)
- [Intention-Adaptive LLM Fine-Tuning for Text Revision Generation](https://mlsanity.com/paper/2602.00477/) (2026)

## Abstract (arXiv)

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.