LLM Fine-Tuning

LLM: Large Language Model

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42 papers in the last four weeks, up 83% on the four weeks before. 0.4% of all new papers.

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Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety
May 13, 2026Qian Shen, Fanghua Cao, Min Yao +3LLM Fine-Tuning Controllable Text Generation
University of Florida, Gainesville, USA · University of Florida
Fine-tuning with Hierarchical Prompting for Robust Propaganda Classification Across Annotation Schemas
May 13, 2026Lukas Stähelin, Veronika Solopova, Max Upravitelev +6LLM Prompting LLM Fine-Tuning
Technische Universität Berlin, QU Lab, XplaiNLP Group, Berlin, Germany · German Research Center for Artificial Intelligence (DFKI), Berlin, Germany · Centre for European Research in Trusted AI (CERTAIN)
LIFT: Last-Mile Fine-Tuning for Table Explicitation
May 13, 2026Divij Khaitan, Ashish TiwariSupervised Fine-Tuning Fine-Tuning
Microsoft Corporation Bangalore, India · Microsoft Corporation Redmond, WA 98052 USA
PRISM-X: Experiments on Personalised Fine-Tuning with Human and Simulated Users
May 13, 2026Hannah Rose Kirk, Liu Leqi, Fanzhi Zeng +4LLM Evaluation LLM Sycophancy
University of Oxford · UK AI Security Institute · University of Texas at Austin +2
STOP: Structured On-Policy Pruning of Long-Form Reasoning in Low-Data Regimes
May 13, 2026Chenjun Xu, Zhennan Zhou, Zhan Su +3LLM Pruning LLM Fine-Tuning
University of Washington · University of Montreal
SLAP: Stratified Loss-based Pruning for On-Policy Data-Efficient Instruction Tuning
May 13, 2026Run Zou, Jianhang Ding, Yifan Ding +3LLM Fine-Tuning Instruction-Tuning Data Selection
Alibaba International Digital Commerce Group, HangZhou, China · Hangzhou Lingju Intelligence AI Lab, Hangzhou, China · Hangzhou Dianzi University, HangZhou, China
Persona-Model Collapse in Emergent Misalignment
May 13, 2026Davi Bastos Costa, Renato VicenteLLM Alignment LLM Fine-Tuning
TELUS Digital Research Hub · Center for Artificial Intelligence and Machine Learning · Institute of Mathematics, Statistics and Computer Science +1
Early Data Exposure Improves Robustness to Subsequent Fine-Tuning
May 12, 2026Lawrence Feng, Gaurav R. Ghosal, Jacob Mitchell Springer +2Language Model Pretraining Continual Learning for LLMs
Carnegie Mellon University
ORBIT: Preserving Foundational Language Capabilities in GenRetrieval via Origin-Regulated Merging
May 12, 2026Neha Verma, Nikhil Mehta, Shao-Chuan Wang +7Continual Learning for LLMs LLM Fine-Tuning
Johns Hopkins University · Google DeepMind · Google
Overtrained, Not Misaligned
May 12, 2026Joel Schreiber, Ariel GoldsteinEarly Stopping LLM Fine-Tuning
Hebrew University of Jerusalem
Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs
May 12, 2026Amr Abourayya, Jens Kleesiek, Michael KampLLM Fine-Tuning Federated Knowledge Distillation
Lamarr Institute for ML and AI, Technical University Dortmund · Institute for AI in medicine (IKIM),University Hospital Essen
Probabilistic Calibration Is a Trainable Capability in Language Models
May 12, 2026Davide Baldelli, Sruthi Kuriakose, Maryam Hashemzadeh +2LLM Fine-Tuning Controllable Text Generation
Chandar Research Lab · Mila – Quebec AI Institute · LAMA-WeST Lab +3
When Emotion Becomes Trigger: Emotion-style dynamic Backdoor Attack Parasitising Large Language Models
May 12, 2026Ziyu Liu, Tao Li, Tianjie Ni +5LLM Backdoor Attacks Backdoor Attacks
School of Cyber Science and Engineering, Sichuan University, Chengdu, China · School of Computer Science, China West Normal University, Nanchong, China · School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou, China
StoicLLM: Preference Optimization for Philosophical Alignment in Small Language Models
May 12, 2026Ishmam Khan, Sindhuja Thogarrati, Shuo ZhangLLM Alignment LLM Fine-Tuning
Tufts University · Bose Corporation
DynaMiCS: Fine-tuning LLMs with Performance Constraints using Dynamic Mixtures
May 11, 2026Eleonora Gualdoni, Sonia Laguna, Louis Bethune +3Fine-Tuning LLM Fine-Tuning
Apple
AdaPaD: Adaptive Parallel Deflation for PEFT with Self-Correcting Rank Discovery
May 11, 2026Barbara Su, Fangshuo Liao, Anastasios KyrillidisLLM Fine-Tuning Adapter Tuning
Department of Computer Science Rice University
DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models
May 11, 2026Haaris Mehmood, Jie Xu, Karthikeyan Saravanan +2Privacy-Preserving Language Models Differentially Private Stochastic Gradient Descent
Samsung R&D Institute UK (SRUK) · Samsung AI Centre Cambridge
FocuSFT: Bilevel Optimization for Dilution-Aware Long-Context Fine-Tuning
May 11, 2026Zehua Pei, Hui-Ling Zhen, Xianzhi Yu +3Supervised Fine-Tuning Self-Attention
The Chinese University of Hong Kong · Huawei Technologies Co., Ltd
Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs
May 11, 2026Wu Li, Yigeng Zhou, Zesheng Shi +3LLM Alignment Self-Play RL
Harbin Institute of Technology, Shenzhen, China · Beijing Academy of Artificial Intelligence, Beijing, China
Concordia: Self-Improving Synthetic Tables for Federated LLMs
May 11, 2026Jimin Huang, Duanyu Feng, Nuo Chen +8LLM Fine-Tuning Synthetic Data Generation
University of Manchester · The Fin AI · National University of Singapore +5
Learning Multi-Indicator Weights for Data Selection: A Joint Task-Model Adaptation Framework with Efficient Proxies
May 10, 2026Jingze Song, Zihao Chen, Wenqing Chen +1LLM Fine-Tuning Instruction-Tuning Data Selection
School of Software Engineering, Sun Yat-sen University
Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications
May 10, 2026Jakob Sturm, Josef Pichlmeier, Christian Bernhard +4LLM Fine-Tuning Cost-Aware Inference
BMW Group; Munich, Germany · Ludwig Maximilians Universitat; Munich, Germany · Technical University of Munich; Munich, Germany
Accelerating Zeroth-Order Spectral Optimization with Partial Orthogonalization from Power Iteration
May 9, 2026Jiahe Chen, Ziye MaZeroth-Order Optimization LLM Fine-Tuning
Department of Computer Science City University of Hong Kong
Few-Shot Truly Benign DPO Attack for Jailbreaking LLMs
May 9, 2026Sangyeon Yoon, Wonje Jeung, Yoonjun Cho +2Direct Preference Optimization LLM Fine-Tuning
Yonsei University
Hint Tuning: Less Data Makes Better Reasoners
May 9, 2026Siqi Fan, Minghao Li, Xiaoqian Ma +6Overthinking in Language Models LLM Fine-Tuning
University of Electronic Science and Technology of China · Xiaohongshu Inc. · National University of Singapore
EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints
May 9, 2026Jiaxiang Geng, Yiyi Lu, Lunyu Zhao +3LLM Evaluation LLM Fine-Tuning
Duke Kunshan University · Flower Labs · University of Cambridge
Rotation-Preserving Supervised Fine-Tuning
May 8, 2026Hangzhan Jin, Tianwei Ni, Lu Li +3Supervised Fine-Tuning Fine-Tuning
1Mila - Quebec AI Institute · 2Polytechnique Montréal · 3Université de Montréal +3
Self-Play Enhancement via Advantage-Weighted Refinement in Online Federated LLM Fine-Tuning
May 8, 2026Seohyun Lee, Wenzhi Fang, Dong-Jun Han +2LLM Fine-Tuning Language Model Self-Improvement
Purdue University · Yonsei University · University at Buffalo-SUNY
Graph Representation Learning Augmented Model Manipulation on Federated Fine-Tuning of LLMs
May 8, 2026Hanlin Cai, Kai Li, Houtianfu Wang +4LLM Fine-Tuning LLM Security
Centre for neXt Communications (CXC), Department of Engineering, University of Cambridge, CB3 0FA Cambridge, U.K. · Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, L-1855, Luxembourg · Telecommunication Networks group (TKN) at the School of Electrical Engineering and Computer Science, TU Berlin, Germany +1
MatryoshkaLoRA: Learning Accurate Hierarchical Low-Rank Representations for LLM Fine-Tuning
May 8, 2026Ionut-Vlad Modoranu, Mher Safaryan, Dan AlistarhLLM Fine-Tuning Hierarchical Representation Learning
Institute of Science and Technology Austria (ISTA). · Lancaster University, UK · ISTA

May 13, 2026, cs.CLJ/K move · Enter open · S save

Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety

Qian Shen, Fanghua Cao, Min Yao, Shlok Gilda+2

University of Florida, Gainesville, USA · University of Florida

LLM Fine-Tuning Controllable Text Generation Text Generation

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Large Language Models (LLMs) are widely applied in educational practices, such as for generating children's stories. However, the generated stories are often too difficult for children to read, and the operational cost of LLMs hinders their widespread adoption in educational settings. We used an existing expert-designed children's reading curriculum and its corresponding generated stories from GPT-4o and Llama 3.3 70B to design different experiments for fine-tuning three 8B-parameter LLMs, which then generated new English reading stories that were subjected to quantitative and qualitative evaluation. Our method prioritizes controllability over scale, enabling educators to target reading levels and error patterns with a compact, affordable model. Our evaluation results show that with appropriate fine-tuning designs, children's English reading stories generated by 8B LLMs perform better on difficulty-related metrics than those from zero-shot GPT-4o and Llama 3.3 70B, with almost no discernible safety issues. Such fine-tuned LLMs could be more broadly used by teachers, parents, and children in classrooms and at home to generate engaging English reading stories with children's interests, controllable difficulty and safety.

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