Synthetic Data Generation for Language Models

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21 papers in the last four weeks, up 110% on the four weeks before. 0.2% of all new papers.

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Simulating Students' Java Programming Errors with Large Language Models
Jun 12, 2026Ali Keramati, Jie Cao, Iman Mohammadi +2LLM Evaluation Programming Education
Utah State University
Automated IEP Generation from Traditional Chinese Parent-Teacher Interviews via Corpus-Grounded Feature Diffusion
Jun 8, 2026Kuanlin Chen, Cheng-En OuAI in Education Generative AI in Education
Department of Industrial Engineering & Engineering Management Chung Yuan Christian University, Taiwan
Better Literary Translation: A Multi-Aspect Data Generation and LLM Training Approach
Jun 4, 2026Zhihao Lin, Ziqi Zhu, Hao Huang +2Synthetic Data Generation for Language Models Language Model Post-Training
1Amazon Web Services (AWS) · 2Peking University
Efficient ASR Training with Conversations that Never Happened
Jun 2, 2026Máté Gedeon, Péter MihajlikSynthetic Data Augmentation Automatic Speech Recognition
Dept. of Telecommunications and Artificial Intelligence, Budapest University of Technology and Economics, Budapest, Hungary · SpeechTex Ltd., Budapest, Hungary · ELTE Research Centre for Linguistics, Budapest, Hungary
Synthetic Hallucinations, Real Gains: Hard Negatives from Frontier Models for FIM Hallucination Mitigation
Jun 2, 2026Mahdi Erfanian, Nelson Daniel Troncoso, Aashna Garg +4LLM Hallucination Mitigation Hard Negative Mining
University of Illinois Chicago · Microsoft
WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents
Jun 1, 2026Hengrui Gu, Xiaotian Han, Kaixiong ZhouTool-Augmented Language Model Agents Tool-Using Agents
North Carolina State University · Case Western Reserve University
Scaling Agentic Capabilities via Grounded Interaction Synthesis
Jun 1, 2026Wenhang Shi, Jinhao Dong, Yiren Chen +4Synthetic Data Generation LLM Agent Training
Renmin University of China · Peking University · Tencent
ContinuousBench: Can Differentially Private Synthetic Text Improve Capabilities?
Jun 1, 2026Peihan Liu, Lucas Rosenblatt, Weiwei Kong +7Privacy-Preserving Language Models Synthetic Benchmark Generation
Columbia University · Work done as a Student Researcher at Google · NYU +4
ANDES: Agent Native Data Evolving Synthesis Tool for Autonomous Instruction Alignment
May 31, 2026Zhengyang Zhao, Shengjie Ye, Lu Ma +3LLM Alignment LLM Agent Skill Learning
Peking University · Sichuan University, Chengdu
Scaling Multi-Hop Training Data via Graph-Constrained Path Selection
May 29, 2026Pengyu Chen, Yonggang Zhang, Mingming Chen +3Multi-Hop QA Synthetic Data Generation for Language Models
The Hong Kong University of Science and Technology · Hong Kong Generative AI Research and Development Center · Hong Kong Baptist University
Combinatorial Synthesis: Scaling Code RLVR via Atomic Decomposition and Recombination
May 29, 2026Jiasheng Zheng, Boxi Cao, Boxi Yu +6RL for Code Generation Reinforcement Learning with Verifiable Rewards
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences · Lero the Research Ireland Centre for Software, University of Limerick · The Chinese University of Hong Kong, Shenzhen +1
Differentially Private Preference Data Synthesis for Large Language Model Alignment
May 29, 2026Fengyu Gao, Jing YangPairwise Preference Learning LLM Alignment
Department of Computer Science, University of Virginia, Charlottesville, Virginia, USA · Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, Virginia, USA.
Exploring Autonomous Agentic Data Engineering for Model Specialization
May 28, 2026Yujie Luo, Xiangyuan Ru, Jingsheng Zheng +10Large Language Model-Guided Optimization Synthetic Data Generation for Language Models
Zhejiang University · Platform and Content Group, Tencent
Domain-Specific Data Synthesis for LLMs via Minimal Sufficient Representation Learning
May 28, 2026Tong Ye, Hang Yu, Tengfei Ma +6Domain Adaptation Synthetic Data Generation
vivo AI Lab, Ant Group, Zhejiang University
Make LLM Learn to Synthesize from Streaming Experiences through Feedback
May 28, 2026Zhenlin Hu, Yan Wang, Zhen Bi +7Continual Learning for LLMs Synthetic Data Generation
Huzhou Normal University · Alibaba Group · Zhejiang Key Laboratory of Intelligent Education Technology and Application +1
LiteCoder-Terminal: Scaling Long-Horizon Terminal Environments for Learning Language Agents
May 28, 2026Xiaoxuan Peng, Kaiqi Zhang, Xinyu Lu +5RL for Language Models Terminal Agents
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences
Activation Steering for Synthetic Data Generation: The Role of Diversity in Downstream Safety Detection
May 27, 2026Vijeta Deshpande, Tootiya Giyahchi, Veena Padmanabhan +2Synthetic Data Generation Synthetic Data Generation for Language Models
UMass Lowell · Amazon · Amazon, CMU +1
Context-Instrumental Data Distillation for Kubernetes Manifest Generation: Method and Experimental Evaluation
May 25, 2026Andrey Kozachok, Anatoliy Bakaev, Aleksandr Kozachok +2LLM Fine-Tuning Code Generation
RTU MIREA, Moscow, Russia
BC Protocol: Structured Dual-Expert Dialogue for Eliciting High-Quality Chain-of-Thought Post-Training Data
May 25, 2026Bo Zou, Chao XuSynthetic Data Generation for Language Models LLM Post-Training
Re-defining Humor Data Objects for AI Humor Research
May 24, 2026Anna Arnett, Bang Nguyen, Meng JiangLLM Prompting Synthetic Data Generation for Language Models
Department of Computer Science and Engineering, University of Notre Dame
STREAM: A Data-Centric Framework for Mining High-Value Task-Oriented Dialogues from Streaming Media
May 24, 2026Liang Xue, Haoyu Liu, Cheng Wang +3Spoken Dialogue Systems Synthetic Data Generation for Language Models
Harbin Institute of Technology Byering Technology Harbin, China · Harbin Institute of Technology Harbin, China
TRACE: A taxonomy-grounded synthetic dataset for teaching-program generation and session interpretation in Applied Behavior Analysis
May 24, 2026Festus KahunlaSynthetic Data Generation Synthetic Data Generation for Language Models
Drexel University · Pombo Labs
Towards a Universal Causal Reasoner
May 24, 2026Qirun Dai, Xiao Liu, Jiawei Zhang +3Causal Reasoning in Language Models Causal Reasoning
1The University of Chicago · University of Illinois Urbana-Champaign
When Does Synthetic Patent Data Help? Volume-Fidelity Trade-offs in Low-Resource Multi-Label Classification
May 22, 2026Amirhossein Yousefiramandi, Ciaran CooneySynthetic Data Augmentation Multi-Label Classification
Clarivate, Intellectual Property · Barcelona, Spain 08025
QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks
May 22, 2026Jian Xie, Tianhe Lin, Zilu Wang +16Deep Research Agents LLM Agent Training
1The Ohio State University · 2Amazon AGI SF Lab
ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning
May 22, 2026Xiaoyuan Li, Keqin Bao, Moxin Li +5RL for Language Models Reinforcement Learning
University of Science and Technology of China · Alibaba Group · National University of Singapore
Psy-Chronicle:A Structured Pipeline for Synthesizing Long-Horizon Campus Psychological Counseling Dialogues
May 21, 2026Chaogui GouMental Health Counseling Emotional Support Conversation
University of Science and Technology Beijing
From Lexicon to AI: A Structured-Data Pipeline for Specialized Conversational Systems in Low-Resource Languages
May 21, 2026Siddhant Hitesh Mantri, Dhara Gorasiya, Malhar Kulkarni +1Knowledge Augmentation for Language Models Synthetic Data Generation for Language Models
NMIMS, Mumbai · CFILT, IIT Bombay
MindLoom: Composing Thought Modes for Frontier-Level Reasoning Data Synthesis
May 20, 2026Haiyang Shen, Taian Guo, Xuanzhong Chen +11Synthetic Data Generation Structured Reasoning
1Peking University · 2Tsinghua University
Self-Verified Distillation: Your Language Model Is Secretly Its Own Synthetic Data Pipeline
May 20, 2026Tony Lee, Percy LiangSelf-Training for Language Models Language Model Self-Assessment
Stanford University

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Simulating Students' Java Programming Errors with Large Language Models

Ali Keramati, Jie Cao, Iman Mohammadi, Mark Warschauer+1

Utah State University

LLM Evaluation Programming Education Generative AI in Education Synthetic Data Generation for Language Models

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Understanding student errors in the programming is a cornerstone of programming education, yet obtaining a representative set of student errors for any newly designed task remains slow and costly, since authentic submissions only accumulate after extensive classroom deployment. This paper explores whether large language models (LLMs) can serve as scalable proxies for students by simulating realistic logical errors in code submissions. Using the CodeWorkout dataset of 74,000+ unique student Java submissions across 37 problems, we evaluate five LLMs under three mainstream prompting strategies: Input-Output (IO), Chain-of-Thought (CoT), and iterative Self-Refine. We assess performance along two key dimensions: diversity (the range of distinct error patterns) and alignment (alignment with authentic student mistakes), and examine how these vary by struggling level of programming tasks. Our quantitative findings reveal that while all models generate diverse errors, their alignment to human submissions diverges: Claude Sonnet 4 achieves the most balanced performance. In addition, we conducted a blinded expert annotation study (N = 401) comparing synthetic and authentic errors. This qualitative analysis confirms that the generated errors are functionally indistinguishable from authentic student errors. Moreover, higher-struggling-level problems elicit more diverse but less student-like errors. These results highlight trade-offs in using LLMs to simulate human learners and suggest design considerations for integrating synthetic errors into teachable agents, intelligent tutoring systems, and large-scale learning analytics.

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