Cover Image

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

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Shen, Qian, Cao, Fanghua, Yao, Min, Gilda, Shlok, Dorr, Bonnie J., Leite, Walter L.
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Artificial Intelligence Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Structured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants
by: Gilda, Sankalp, et al.
Published: (2026)

AI-Assisted Engineering Should Track the Epistemic Status and Temporal Validity of Architectural Decisions
by: Gilda, Sankalp, et al.
Published: (2026)

On-Policy Supervised Fine-Tuning for Efficient Reasoning
by: Zhao, Anhao, et al.
Published: (2026)

Multilingual Target-Stance Extraction
by: Mines, Ethan, et al.
Published: (2025)

Filling the Gap: Is Commonsense Knowledge Generation useful for Natural Language Inference?
by: Jayaweera, Chathuri, et al.
Published: (2025)

Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning
by: Liu, Siyuan, et al.
Published: (2026)

The Effect of Data Partitioning Strategy on Model Generalizability: A Case Study of Morphological Segmentation
by: Liu, Zoey, et al.
Published: (2024)

BLADE: Better Language Answers through Dialogue and Explanations
by: Jayaweera, Chathuri, et al.
Published: (2026)

From Disagreement to Understanding: The Case for Ambiguity Detection in NLI
by: Jayaweera, Chathuri, et al.
Published: (2025)

Advancing Question Generation with Joint Narrative and Difficulty Control
by: Leite, Bernardo, et al.
Published: (2025)

Understanding and Preserving Safety in Fine-Tuned LLMs
by: Zhang, Jiawen, et al.
Published: (2026)

Instruction Tuning for Story Understanding and Generation with Weak Supervision
by: Yuan, Yangshu, et al.
Published: (2025)

Rethinking Data Selection for Supervised Fine-Tuning
by: Shen, Ming
Published: (2024)

Curvature-Aware Safety Restoration In LLMs Fine-Tuning
by: Bach, Thong, et al.
Published: (2025)

AMREx: AMR for Explainable Fact Verification
by: Jayaweera, Chathuri, et al.
Published: (2024)

NeuronTune: Fine-Grained Neuron Modulation for Balanced Safety-Utility Alignment in LLMs
by: Pan, Birong, et al.
Published: (2025)

Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education
by: Vassar, Alexandra, et al.
Published: (2024)

RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs
by: Asif, Sadia, et al.
Published: (2026)

Benign Fine-Tuning Breaks Safety Alignment in Audio LLMs
by: Roh, Jaechul, et al.
Published: (2026)

Brain Structural Connectivity and Morphological Awareness Mediate the Association Between Home Literacy Environment and Reading Outcomes in Children With Family History of Reading Difficulties
by: Xianglin Zhang, et al.
Published: (2025)

HerStory Fine Tuning Transnational Report
by: Kyttaro Enallaktikon Anazitiseon Neon (K.E.A.N)
Published: (2025)

HerStory Fine Tuning Transnational Report
by: Kyttaro Enallaktikon Anazitiseon Neon (K.E.A.N)
Published: (2024)

Revisiting Semantic Role Labeling: Efficient Structured Inference with Dependency-Informed Analysis
by: Youm, Sangpil, et al.
Published: (2026)

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation
by: Chen, Zihong, et al.
Published: (2025)

Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data
by: Ling, Zhenqing, et al.
Published: (2025)

Supervised Fine-Tuning or In-Context Learning? Evaluating LLMs for Clinical NER
by: Baroian, Andrei
Published: (2025)

Fine-Tuning LLMs for Report Summarization: Analysis on Supervised and Unsupervised Data
by: Rallapalli, Swati, et al.
Published: (2025)

Supervised Fine-Tuning LLMs to Behave as Pedagogical Agents in Programming Education
by: Ross, Emily, et al.
Published: (2025)

Enhancing Non-English Capabilities of English-Centric Large Language Models through Deep Supervision Fine-Tuning
by: Huo, Wenshuai, et al.
Published: (2025)

Safety Mirage: How Spurious Correlations Undermine VLM Safety Fine-Tuning and Can Be Mitigated by Machine Unlearning
by: Chen, Yiwei, et al.
Published: (2025)

PEFT-as-an-Attack! Jailbreaking Language Models during Federated Parameter-Efficient Fine-Tuning
by: Li, Shenghui, et al.
Published: (2024)

Supervised In-Context Fine-Tuning for Generative Sequence Labeling
by: Dukić, David, et al.
Published: (2025)

Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
by: Pang, Jinlong, et al.
Published: (2025)

Anchored Supervised Fine-Tuning
by: Zhu, He, et al.
Published: (2025)

Proximal Supervised Fine-Tuning
by: Zhu, Wenhong, et al.
Published: (2025)

Difficulty-Aware Score Generation for Piano Sight-Reading
by: Ramoneda, Pedro, et al.
Published: (2025)

Rethinking Bottlenecks in Safety Fine-Tuning of Vision Language Models
by: Ding, Yi, et al.
Published: (2025)

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs
by: Pareja, Aldo, et al.
Published: (2024)

One-Token Rollout: Guiding Supervised Fine-Tuning of LLMs with Policy Gradient
by: Ming, Rui, et al.
Published: (2025)

Silent Sabotage During Fine-Tuning: Few-Shot Rationale Poisoning of Compact Medical LLMs
by: Xie, Jingyuan, et al.
Published: (2026)