Data Selection for Language Model Fine-Tuning

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25 papers9 papers in the last four weeks, with none the four weeks before. 0.1% of all new papers.

Perturbation-Based Data Selection for Medical Question Answering
Oct 24, 2026Yuexin Wu, Dayou Yu, Vasile RusData Selection Data Selection for Language Model Fine-Tuning
Department of Computer Science, University of Memphis, Memphis, TN, USA
ICLHerd: Herding-Based Data Selection for LLMs with In-Context Learning as a Proxy
Oct 19, 2026Yuqiao Wen, Lili MouTraining Data Selection Data Selection
Dept. Computing Science & Alberta Machine Intelligence Institute (Amii), University of Alberta · Canada CIFAR AI Chair
Is it RL or Data? A Controlled Comparison of GRPO and Rejection-Sampling Fine-Tuning
Oct 19, 2026Lakshya ChaudhryFine-Tuning RL Fine-Tuning
Purdue University
NADS: Navigator-Guided Data Selection for Mitigating Catastrophic Forgetting in Fine-Tuning
Oct 19, 2026Jianhao Zhang, Ou Wu, Yi DuLLM Fine-Tuning Data Selection for Language Model Fine-Tuning
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China · Computer Network Information Center, Chinese Academy of Sciences, Beijing, China
DeGMatch: Target-Aware Reasoning Data Selection via Deconfounded Gradient Matching
Oct 18, 2026Changho Lee, Janghoon Han, Rajdeep Mukherjee +4Data Selection for Language Model Fine-Tuning LLM Reasoning
University of Michigan · University of Texas at Dallas · LG AI Research
Which and When to Admit: Gradient Admission for Data-Centric Small Language Model Finetuning
Oct 6, 2026Hongyu Cao, Yanchi Liu, Kunpeng Liu +5Gradient Interference Low-Rank Adaptation
Arizona State University · Clemson University · Meta AI +1
LESSER: Post-Training Data Selection with Output-Layer Gradients
Oct 2, 2026Lyuxin David Zhang, Eric Wong, Surbhi Goel +1Training Data Selection Data Selection for Language Model Fine-Tuning
University of Pennsylvania · University of Texas at Austin
Every Batch Is Its Own Validation Set: Leave-One-Out Gradient Matching for Online Data Selection in LLM Fine-Tuning
Sep 30, 2026Hongyu Chen, Xinyi Luo, Ming Zhao +6LLM Fine-Tuning Data Selection for Language Model Fine-Tuning
Sichuan University, Chengdu, China · University of Electronic Science and Technology of China, Chengdu, China
A helps B while B hurts A: directed transfer in instruction-tuning mixture
Sep 30, 2026Nima H. Siboni, Vahid RostamiLLM Fine-Tuning Instruction-Tuning Data Selection
Juna.ai, Kastanienallee 32, 10435 Berlin, Germany · Computational Systems Neuroscience, Institute of Zoology, University of Cologne, Germany
PPL-Factory: Task-Aware and Budget-Aware Data Selection from Language Modeling to Reasoning
Jul 20, 2026Hang Zhang, Warren J. GrossLLM Fine-Tuning Efficient Language Model Training
Department of Electrical and Computer Engineering, McGill University Montreal, QC, Canada
HARP: Efficient Data Selection for Finetuning Large Language Models
Jun 5, 2026Ning Wang, Zhengxin Zhang, Maosen Tang +3LLM Fine-Tuning Active Learning
Cornell University · The Hong Kong University of Science and Technology
Two to Tango: Coupled Task-Reference Selection for Safe LLM Fine-tuning
Jun 1, 2026Xinrui Chen, Jianhao Zhang, Ou Wu +1LLM Fine-Tuning Data Selection
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences
The Long-Term Effects of Data Selection in LLM Fine-Tuning
May 28, 2026Yuxin Yang, Aoxiong Zeng, Xiangquan YangContinual Learning for LLMs LLM Fine-Tuning
Shanghai University · East China Normal University
From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning
May 13, 2026Haodong Wu, Jiahao Zhang, Lijie Hu +1Supervised Fine-Tuning Training Data Curation
The Hong Kong University of Science and Technology (Guangzhou) · Mohamed bin Zayed University of Artificial Intelligence
One Algorithm, Two Goals: Dual Scoring for Parameter and Data Selection in LLM Fine-Tuning
May 7, 2026Xinrui Chen, Liu Yang, Ou WuSupervised Fine-Tuning LLM Fine-Tuning
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences · College of Intelligence and Computing, Tianjin University
From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models
Apr 28, 2026Ling Shi, Xinwei Wu, Xiaohu Zhao +7LLM Fine-Tuning Data Selection for Language Model Fine-Tuning
TJUNLP Lab, School of Computer Science and Technology, Tianjin University, China · Alibaba Group, China
CRAFT: Clustered Regression for Adaptive Filtering of Training data
Apr 24, 2026Parthasarathi Panda, Asheswari Swain, Subhrakanta PandaData Selection Training Data Selection
1Google, USA · 2,3Department of Computer Science & Information Systems, BITS Pilani, Hyderabad, India
COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling
Apr 22, 2026Noah FlynnMultilingual Language Models Continual Learning for LLMs
UC Berkeley
Aligning the Query Space: Greedy Information Projection for Language Model Data Selection
Mar 14, 2026Victor Ye Dong, Kuan-Yun Lee, Jiamei Shuai +3LLM Fine-Tuning Instruction-Tuning Data Selection
Microsoft
Filter-then-Weight: Online Data Selection and Reweighting for LLM Fine-Tuning
Mar 8, 2026Fangxin Wang, Peyman Baghershahi, Langzhou He +3Data Selection for Language Model Fine-Tuning Training Data Selection
Department of Computer Science, University of Illinois Chicago
Theoretical Perspectives on Data Quality and Synergistic Effects in Pre- and Post-Training Reasoning Models
Mar 1, 2026Adel Javanmard, Baharan Mirzasoleiman, Vahab MirrokniData Selection for Language Model Fine-Tuning LLM Fine-Tuning
University of Southern California · Google Research · University of California Los Angeles
Entropy-Based Data Selection for Language Models
Feb 19, 2026Hongming Li, Yang Liu, Chao HuangData Selection for Language Model Fine-Tuning Training Data Selection
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China · National Key Laboratory of General Artificial Intelligence, Beijing Institute for General Artificial Intelligence, Beijing 100086, China · Shunde Innovation School, University of Science and Technology Beijing, Foshan 528399, China
A Critical Look at Targeted Instruction Selection: Disentangling What Matters (and What Doesn't)
Feb 16, 2026Nihal V. Nayak, Paula Rodriguez-Diaz, Neha Hulkund +2Instruction-Tuning Data Selection Training Data Selection
Harvard University · MIT · Kempner Institute
The Role of Dataset Linguistic Structure in the Cultural Awareness of Large Language Models
Feb 1, 2026Reem I. Masoud, Chen Feng, Shunta Asano +3LLM Fine-Tuning Cultural Alignment
University College London · King Abdulaziz University · Queen’s University Belfast +3
Bilevel Data Curation for LLM Fine-tuning: Offline Selection and Online Self-Refining Generation
Nov 26, 2025Quan Xiao, Yutong Xuan, Gaowen Liu +2Supervised Fine-Tuning LLM Self-Refinement
Cornell University · Cisco Research

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Perturbation-Based Data Selection for Medical Question Answering

Yuexin Wu, Dayou Yu, Vasile Rus

Department of Computer Science, University of Memphis, Memphis, TN, USA

Data Selection Data Selection for Language Model Fine-Tuning Biomedical QA Medical LLMs

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Selecting a compact training subset is an important step in adapting medical language models from existing question-answering banks. We investigate perturbation-based data selection using two signals derived from Gaussian perturbations of the final-block representation: prediction-change frequency and non-flipping margin contraction. After a shared entropy prefilter, Representation-Sensitivity Prioritization (RSP) combines the signals using automatic root-mean-square scaling, while RSP-Drop ranks candidates by contraction alone. Both selectors use only questions and answer options during selection. We evaluate them with MedGemma-4B on five medical QA datasets at retained-set budgets of 64, 128, and 256 examples. At the primary budget of 128, RSP and RSP-Drop obtain dataset-averaged Macro-F1 scores of 47.55 and 48.28, respectively, compared with 45.11 for entropy-pool-matched random selection. Their relative performance varies across datasets and budgets. A selected-set case study identifies contrasting clean-margin profiles among the examples exchanged by the two rules, while a full-pool ablation shows that entropy prefiltering is not uniformly beneficial. These retrospective results characterize the empirical utility and limitations of perturbation-based selection under a fixed adaptation protocol. Evaluation uses one backbone and benchmarks with documented training-family overlap; it does not establish clinical effectiveness.