Large Language Model-Based Data Augmentation

Latest papers

22 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection
Oct 9, 2026Ziyu Luo, Wei Li, Haoping Huang +2Large Language Model-Based Data Augmentation Fake News Detection
Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, Chongqing 401331, China · School of Big Data and Software Engineering, Chongqing University, Chongqing 400044, China · Department of Plastic Surgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing 400000, China +1
Colorectal Cancer Segmentation with Adaptive Augmentation and Multi-Resolution Ensemble Models
Sep 29, 2026Ümit Mert Çağlar, Alptekin TemizelHistopathology Image Segmentation Large Language Model-Based Data Augmentation
Graduate School of Informatics, METU, Ankara, Turkey
DualGuard: Dual-Mode Quality Control for Logic-Preserving Data Augmentation
Sep 26, 2026Shenghao Li, Lin ZhaoData Augmentation Synthetic Data Augmentation
Guangzhou University, Guangzhou, China
Dictionary-Constrained Grapheme-to-Phoneme for Unsegmented Languages from LLM-Annotated Data
Sep 17, 2026Rui Hu, Zhenpeng Zhan, Xiaolong LinLarge Language Model-Based Data Augmentation G2P Conversion
Baidu Inc., Shenzhen, China
REER-PT: Reverse-Engineered Reasoning for Perplexity-Guided Pre-training Data Augmentation
Aug 31, 2026Haoran Que, Jiajun Shi, Ting Huang +7Language Model Pretraining Synthetic Data Pretraining
Peking University · ByteDance Seed · Work done at ByteDance Seed +1
TEAMMix: Taxonomy Enrichment Augmentation and Minority-augmented Mixing Strategy for LLM-enhanced Weak-Supervised Hierarchical Text Classification
Aug 11, 2026Jian Zhang, Zhuohao Yang, Songlin Lei +6Class-Imbalanced Learning Hierarchical Classification
School of Computer Science and Technology, Zhejiang University, Hangzhou, China · ZJU-UIUC Institute, Zhejiang University, Haining, China · Shaoxing K3i Technology Co. Ltd +1
Want Better Synthetic Data? Steer It: Activation Steering for Low-Resource Language Generation
Jun 16, 2026Jan Cegin, Daniil Gurgurov, Yusser Al Ghussin +1Language Model Steering Synthetic Data Generation for Language Models
Kempelen Institute of Intelligent Technologies, Bratislava, Slovakia · German Research Institute for Artificial Intelligence (DFKI), Saarbrücken, Germany
Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languages
May 28, 2026David Rey-Blanco, Roberto CruzData Augmentation Healthcare
TietAI
EmbGen: Teaching with Reassembled Corpora
May 19, 2026Arun K Lenin, Kai Rouse, Andrea Nicastro +1Synthetic Instruction Generation Domain Adaptation for LLMs
Commonwealth Bank of Australia Sydney, Australia
Can Large Language Models Imitate Human Speech for Clinical Assessment? LLM-Driven Data Augmentation for Cognitive Score Prediction
May 15, 2026Si-Belkacem Yamine Ketir, Lenard Paulo Tamayo, Shohei Hisada +3Class-Imbalanced Learning Synthetic Data Augmentation
Télécom SudParis, France · Nara Institute of Science and Technology, Japan
AcquisitionSynthesis: Targeted Data Generation using Acquisition Functions
May 13, 2026Ishika Agarwal, Sofia Stoica, Emre Can Acikgoz +4Synthetic Data Generation Synthetic Data Generation for Language Models
University of Illinois Urbana-Champaign1 · Amazon2
Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation
Apr 23, 2026Minping Chen, Bing Xu, Zulong Chen +4Data Augmentation Mixture of Experts
HKUST (GZ) · Alibaba Group · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) +3
Enhancing Science Classroom Discourse Analysis through Joint Multi-Task Learning for Reasoning-Component Classification
Apr 22, 2026Jiho Noh, Mukhesh Raghava Katragadda, Raymond Carl +1AI in Education Multi-Task Learning
Department of Computer Science, Kennesaw State University, Kennesaw, GA 30144 USA · Bagwell College of Education, Kennesaw State University, Kennesaw, GA 30144 USA
LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models
Apr 20, 2026Pranshav Gajjar, Manan Tiwari, Sayanta Seth +1Distribution Shift Robustness Synthetic Data Augmentation
NextG Wireless Lab, North Carolina State University, NC
When Does Data Augmentation Help? Evaluating LLM and Back-Translation Methods for Hausa and Fongbe NLP
Apr 14, 2026Mahounan Pericles Adjovi, Roald Eiselen, Prasenjit MitraSynthetic Data Augmentation Large Language Model-Based Data Augmentation
Carnegie Mellon University Africa, Kigali, Rwanda · Centre for Text Technology, North-West University, Potchefstroom, South Africa
NameBERT: Scaling Name-Based Nationality Classification with LLM-Augmented Open Academic Data
Apr 12, 2026Cong Ming, Ruixin Shi, Yifan HuBidirectional Encoder Representations from Transformers Large Language Model-Based Data Augmentation
Northeastern University, United States
Translation Asymmetry in LLMs as a Data Augmentation Factor: A Case Study for 6 Romansh Language Varieties
Mar 26, 2026Jannis Vamvas, Ignacio Pérez Prat, Angela Heldstab +3Machine Translation Low-Resource MT
University of Zurich · Lia Rumantscha
Synthetic Mixed Training: Scaling Parametric Knowledge Acquisition Beyond RAG
Mar 24, 2026Seungju Han, Konwoo Kim, Chanwoo Park +5Knowledge Augmentation for Language Models Synthetic Data Augmentation
Stanford University · MIT · University of Washington
SPA: A Simple but Tough-to-Beat Baseline for Knowledge Injection
Mar 23, 2026Kexian Tang, Jiani Wang, Shaowen Wang +1Knowledge Augmentation for Language Models Synthetic Data Generation for Language Models
Institute for Interdisciplinary Information Sciences, Tsinghua University
Synthetic Data Domain Adaptation for ASR via LLM-based Text and Phonetic Respelling Augmentation
Mar 11, 2026Natsuo Yamashita, Koichi Nagatsuka, Hiroaki Kokubo +2Large Language Model-Based Data Augmentation Domain Adaptation for ASR
Hitachi, Ltd.
SAIL: Similarity-Aware Guidance and Inter-Caption Augmentation-based Learning for Weakly-Supervised Dense Video Captioning
Mar 5, 2026Ye-Chan Kim, SeungJu Cha, Si-Woo Kim +3Large Language Model-Based Data Augmentation Video Captioning
Hanyang University, South Korea
Label-Consistent Data Generation for Aspect-Based Sentiment Analysis Using LLM Agents
Feb 18, 2026Mohammad H. A. Monfared, Lucie Flek, Akbar KarimiSynthetic Data Augmentation Large Language Model-Based Data Augmentation
Bonn-Aachen International Center for Information Technology, University of Bonn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence, Germany

Oct 9, 2026J/K move · Enter open · S save

SharP: Soft and Hard Prompt-Guided Augmentation with LLM for Low Resource Fake News Detection

Ziyu Luo, Wei Li, Haoping Huang, Kecheng Liu+1

Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, Chongqing 401331, China · School of Big Data and Software Engineering, Chongqing University, Chongqing 400044, China · Department of Plastic Surgery, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing 400000, China+1

Large Language Model-Based Data Augmentation Fake News Detection Synthetic Data Augmentation Data Augmentation Prompt Learning

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Fake news detection under low resource conditions is challenged by the scarcity of labeled data and the difficulty of capturing subtle deceptive patterns. In this work, we focus on low resource fake news detection, where only a small labeled set is available for training, while evaluation is performed under a fixed and unchanged test protocol. This setting is motivated by costly, ambiguous fact-checking and emerging topics and domains that are poorly covered by existing benchmarks. Existing data augmentation methods, such as synonym substitution, generative adversarial networks, and large language model (LLM)-based generation, often produce fluent but overly generic content, lacking the task-specific relevance needed for accurate detection. To address this, we propose SharP, a boundary-aware text generation framework guided by soft and hard prompts, which leverages LLMs as tools adapted to produce task-specific synthetic samples. SharP employs a dual prompt mechanism: hard prompts provide structured guidance and factual constraints, while soft prompts introduce learned semantic patterns from the data. These two types of prompts are combined to guide the generation process, enabling the model to produce samples that closely align with the characteristics of news. To further enhance detection performance, we incorporate a boundary-aware strategy that steers generation toward areas where the classifier is less confident, helping to clarify subtle distinctions between real and fake content. In addition, we adopt a dual objective optimization that balances semantic alignment with the source data and classifier feedback. This encourages the generation of samples that are both domain-consistent and helpful for refining decision boundaries, ultimately improving model robustness and generalization in low resource fake news detection.