arXiv Machine Learning July 15, 2026 By Tri-Nhan Vo, Dang Nguyen, Sunil Gupta

TAKE: Trajectory-Aware Knowledge Estimation for Text Dataset Distillation

Read the original on arXiv Machine Learning →

arXiv:2607. 11898v1 Announce Type: cross Abstract: Large-scale text corpora have become a quiet bottleneck in modern NLP, not just in storage, but in the accumulated cost of training, fine-tuning, and continual learning.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Hugging Face Trending Papers

Aug 24

Cross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Data

Structured data exists in many forms (tables, knowledge graphs, charts, and time series), and converting it into text may involve different generation tasks. However, most prior work on data-to-text (...

llms fine-tuning efficiency benchmarks

arXiv AI

Aug 25

Cross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Data

arXiv:2608.23391v1 Announce Type: cross Abstract: Structured data exists in many forms (tables, knowledge graphs, charts, and time series), and converting it into text may involve different generatio...

By Yifei Song, Kun Efimov-Zhang, Claire Gardent

llms fine-tuning efficiency benchmarks

arXiv AI

Jun 18

DRIFT: Refining Instruction Data via On-Policy Data Attribution

arXiv:2606. 18307v1 Announce Type: cross Abstract: Optimizing the training data distribution for Supervised Fine-Tuning (SFT) dictates the capability of Large Language Models (LLMs).

By Zefan Wang, Lincheng Li, Tianyu Yu, Yuan Yao

llms fine-tuning safety

arXiv AI

Jun 24

Scaling Laws for Task-Specific LLM Distillation

arXiv:2606. 24747v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance across a growing range of domains, yet their scale poses deployment challenges in applications where latency and cost constraints are critical.

By Lavinia Ghita, Dhruv Desai, Ioana Boier

llms fine-tuning efficiency benchmarks

arXiv Machine Learning

Jul 24

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators

arXiv:2607. 20465v1 Announce Type: new Abstract: The quality of training data fundamentally determines the capabilities of large language models (LLMs), yet no unified benchmark exists to measure how well LLMs, agents, and data-centric workflows actually prepare training data end to end.

By Hao Liang, Qifeng Cai, Yibo Lin, Jianzhuo Du, Qifeng Xia, Sizhe Qiu, Linzhuang Sun, Meiyi Qiang, Zhaoyang Han, Xiaochen Ma, Bohan Zeng, Ruichuan An, Conghui He, Wentao Zhang

llms agents fine-tuning benchmarks safety data-engineering

arXiv AI

Jun 16

Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift

arXiv:2606. 15734v1 Announce Type: cross Abstract: Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters can accumulate weight drift, potentially causing catastrophic forgetting and degrading general capabilities.

By Weihang Su, Jiacheng Kang, Jingyan Xu, Qingyao Ai, Jianming Long, Hanwen Zhang, Bangde Du, Xinyuan Cao, Min Zhang, Yiqun Liu

rag

One email a morning, machine-written

Related stories

Cross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Data

Cross-Domain, Multi-Task Data-to-Text Generation without In-Domain Training Data

DRIFT: Refining Instruction Data via On-Policy Data Attribution

Scaling Laws for Task-Specific LLM Distillation

DataPrep-Bench: Benchmarking LLMs as Training Data Preparators

Retrievable Gradients: Continual Post-Training Without Cumulative Weight Drift