arXiv AI August 11, 2026 By Kexian Tang, Jiani Wang, Shaowen Wang, Kaifeng Lyu

SPA: A Simple but Tough-to-Beat Baseline for Knowledge Injection

arXiv:2603. 22213v2 Announce Type: replace-cross Abstract: While large language models (LLMs) are pretrained on massive amounts of data, their knowledge coverage remains incomplete in specialized, data-scarce domains, motivating extensive efforts to study synthetic data generation for knowledge injection.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 31

How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data

arXiv:2604. 13977v2 Announce Type: replace-cross Abstract: Synthetic data is a standard component in training large language models, yet systematic comparisons across design dimensions, including rephrasing strategy, generator model, and source data, remain absent.

By Joel Niklaus, Atsuki Yamaguchi, Michal \v{S}tef\'anik, Guilherme Penedo, Hynek Kydl\'i\v{c}ek, Elie Bakouch, Lewis Tunstall, Edward Emanuel Beeching, Thibaud Frere, Colin Raffel, Leandro von Werra, Thomas Wolf

llms

arXiv AI

Jun 16

Data Augmentations for Data-Constrained Language Model Pretraining

arXiv:2606. 16246v1 Announce Type: cross Abstract: As AI labs approach a data ceiling where compute capacity outpaces the rate of new high-quality text generation, language model pretraining is shifting toward a data-constrained, compute-abundant regime that demands productive multi-epoch training on fixed corpora.

By Michael K. Chen, Xikun Zhang, Zhen Wang

llms

arXiv Machine Learning

Jul 14

FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale

arXiv:2601. 22146v2 Announce Type: replace-cross Abstract: Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised "predict the next word" objective on a vast amount of unstructured text data.

By Ajay Patel, Colin Raffel, Chris Callison-Burch

llms benchmarks

arXiv AI

Jun 4

ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models

arXiv:2605. 18879v3 Announce Type: replace-cross Abstract: Large language models inevitably retain sensitive information, defined as inputs that may induce harmful generations, due to training on massive web corpora, raising concerns for privacy and safety.

By Yujie Lin, Chengyi Yang, Zhishang Xiang, Yiping Song, Jinsong Su

llms fine-tuning safety

arXiv Machine Learning

Aug 6

From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs

arXiv:2601. 03808v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have achieved notable performance in code synthesis; however, data-aware augmentation remains a limiting factor, handled via heuristic design or brute-force approaches.

By Usha Shrestha, Dmitry Ignatov, Radu Timofte

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

1d ago

LLMs Get Smarter from Targeted Synthetic Multilingual Data

arXiv:2608. 15964v1 Announce Type: cross Abstract: Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt.

By Ishika Agarwal, Arkajyoti Charaborty, Tanner Sorensen, Neha Gupta, Andreas Stolcke

llms fine-tuning

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How Can We Synthesize High-Quality Pretraining Data? A Systematic Study of Prompt Design, Generator Model, and Source Data

Data Augmentations for Data-Constrained Language Model Pretraining

FineInstructions: Scaling Synthetic Instructions to Pre-Training Scale

ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models

From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs

LLMs Get Smarter from Targeted Synthetic Multilingual Data