Image de couverture de livre

Not All Synthetic Data Is Yours to Learn From

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Alemohammad, Sina, Chen, Li, Baraniuk, Richard G., Wang, Zhangyang
Format:	Preprint
Publié:	2026
Sujets:	Computation and Language Artificial Intelligence Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Neon: Negative Extrapolation From Self-Training Improves Image Generation
par: Alemohammad, Sina, et autres
Publié: (2025)

Self-Improving Diffusion Models with Synthetic Data
par: Alemohammad, Sina, et autres
Publié: (2024)

Minimizing Collateral Damage in Activation Steering
par: Nguyen, Tam, et autres
Publié: (2026)

Improving Direct Persian-English Speech-to-Speech Translation with Discrete Units and Synthetic Parallel Data
par: Rashidi, Sina, et autres
Publié: (2025)

Synthetic Data RL: Task Definition Is All You Need
par: Guo, Yiduo, et autres
Publié: (2025)

SynthDST: Synthetic Data is All You Need for Few-Shot Dialog State Tracking
par: Kulkarni, Atharva, et autres
Publié: (2024)

Synthetic Multimodal Question Generation
par: Wu, Ian, et autres
Publié: (2024)

Synthetic Context Generation for Question Generation
par: Liu, Naiming, et autres
Publié: (2024)

CodecLM: Aligning Language Models with Tailored Synthetic Data
par: Wang, Zifeng, et autres
Publié: (2024)

How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining
par: Luo, Kairong, et autres
Publié: (2025)

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models
par: Havrilla, Alex, et autres
Publié: (2024)

Learning from Synthetic Data Improves Multi-hop Reasoning
par: Kabra, Anmol, et autres
Publié: (2026)

No One-Size-Fits-All: Building Systems For Translation to Bashkir, Kazakh, Kyrgyz, Tatar and Chuvash Using Synthetic And Original Data
par: Karpov, Dmitry
Publié: (2026)

CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation
par: Ziegler, Ingo, et autres
Publié: (2024)

DualAlign: Generating Clinically Grounded Synthetic Data
par: Li, Rumeng, et autres
Publié: (2025)

From Artificial Needles to Real Haystacks: Improving Retrieval Capabilities in LLMs by Finetuning on Synthetic Data
par: Xiong, Zheyang, et autres
Publié: (2024)

LoX: Low-Rank Extrapolation Robustifies LLM Safety Against Fine-tuning
par: Perin, Gabriel J., et autres
Publié: (2025)

Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework
par: Wang, Dong, et autres
Publié: (2025)

Synthetic Data for any Differentiable Target
par: Thrush, Tristan, et autres
Publié: (2026)

DiffLM: Controllable Synthetic Data Generation via Diffusion Language Models
par: Zhou, Ying, et autres
Publié: (2024)

Always Learning, Always Mixing: Efficient and Simple Data Mixing All The Time
par: Hu, Michael Y., et autres
Publié: (2026)

ToolRL: Reward is All Tool Learning Needs
par: Qian, Cheng, et autres
Publié: (2025)

Reasoning-Driven Synthetic Data Generation and Evaluation
par: Davidson, Tim R., et autres
Publié: (2026)

What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
par: Choe, Sang Keun, et autres
Publié: (2024)

SIPDO: Closed-Loop Prompt Optimization via Synthetic Data Feedback
par: Yu, Yaoning, et autres
Publié: (2025)

XL-Suite: Cross-Lingual Synthetic Training and Evaluation Data for Open-Ended Generation
par: Iyer, Vivek, et autres
Publié: (2025)

MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models
par: Pandit, Shrey, et autres
Publié: (2025)

Dynamic Context Evolution for Scalable Synthetic Data Generation
par: Lingo, Ryan, et autres
Publié: (2026)

CasualSynth: Generating Structurally Sound Synthetic Data
par: Cheng, Zehua, et autres
Publié: (2026)

Fill In The Gaps: Model Calibration and Generalization with Synthetic Data
par: Ba, Yang, et autres
Publié: (2024)

Out-of-Distribution Detection using Synthetic Data Generation
par: Abbas, Momin, et autres
Publié: (2025)

SQBC: Active Learning using LLM-Generated Synthetic Data for Stance Detection in Online Political Discussions
par: Wagner, Stefan Sylvius, et autres
Publié: (2024)

BARE: Leveraging Base Language Models for Few-Shot Synthetic Data Generation
par: Zhu, Alan, et autres
Publié: (2025)

Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning
par: Wang, Zhaoyang, et autres
Publié: (2026)

How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse
par: Seddik, Mohamed El Amine, et autres
Publié: (2024)

Lightweight Safety Guardrails via Synthetic Data and RL-guided Adversarial Training
par: Ilin, Aleksei, et autres
Publié: (2025)

SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training
par: Huang, Tianjin, et autres
Publié: (2025)

Subliminal Effects in Your Data: A General Mechanism via Log-Linearity
par: Aden-Ali, Ishaq, et autres
Publié: (2026)

Maximize Your Data's Potential: Enhancing LLM Accuracy with Two-Phase Pretraining
par: Feng, Steven, et autres
Publié: (2024)

Building a Foundational Guardrail for General Agentic Systems via Synthetic Data
par: Huang, Yue, et autres
Publié: (2025)