arXiv Computation and Language

Reclaiming Low-Quality Instruction Data with Rule-Guided LLM Reconstruction

arXiv Machine Learning

Sep 2

Beyond Scores: Understanding LLM-as-a-Judge Mechanisms in Summarization Evaluation

arXiv:2609.01604v1 Announce Type: cross Abstract: LLM-based evaluators of natural language generation (NLG) quality are widely deployed as scoring tools and as automated training signals, yet the int...

By Himil Vasava, Ming Jiang

llms nlp fine-tuning

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

Jul 10

UltraX: Refining Pre-Training Data at Scale with Adaptive Programmatic Editing

arXiv:2607. 08646v1 Announce Type: cross Abstract: As available training data approaches its physical limit, gains from Scaling Laws have begun to diminish.

By Xinlong Zhao, Dongsheng Liu, Hengyu Zhao, Zixuan Fu, Zheng Wang, Jie Cai, Jie Zhou, Qiang Ma, Xuanhe Zhou, Xu Han, Yudong Wang, Zhiyuan Liu

llms safety

arXiv AI

Jun 2

RAFT: Data Refinement and Adaptive Distillation for Domain Fine-Tuning with Alleviated Forgetting

arXiv:2606. 00147v1 Announce Type: cross Abstract: Domain-specific supervised fine-tuning (SFT) often improves in-domain performance at the cost of degrading a model's general capabilities.

By Yuduo Li, Xiaofeng Shi, Qian Kou, Longbin Yu, Hua Zhou

fine-tuning efficiency

Hugging Face Trending Papers

Sep 1

Beyond Scores: Understanding LLM-as-a-Judge Mechanisms in Summarization Evaluation

LLM-based evaluators of natural language generation (NLG) quality are widely deployed as scoring tools and as automated training signals, yet the internal procedure by which they assign a rating remai...

llms nlp fine-tuning

arXiv AI

Jun 2

SkillRevise: Improving LLM-Authored Agent Skills via Trace-Conditioned Skill Revision

arXiv:2606. 01139v1 Announce Type: new Abstract: Agent skills are procedural artifacts that enable LLM agents to execute workflows, verify constraints, and recover from failures.

By Yuxuan Liu, Zhaochen Su, Lingyun Xie, Yuhao Zhang, Qing Zong, Jiahe Guo, Zhongwei Xie, Yiyan Ji, Yauwai Yim, Hongyu Luo, Xiyu Ren, Ruan Chenyu, Haoran Li, Yangqiu Song

llms agents benchmarks

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 Machine Learning

Jul 7

Train Smarter, Not Longer: Memorization-Guided Data Reuse for Efficient LLM Training

arXiv:2607. 04969v1 Announce Type: new Abstract: The training paradigm of large language models has shifted from traditional one-pass training to multi-epoch training, as reasonable reuse of limited high-quality data can improve both model performance and sample efficiency.

By Jingwei Zuo, Cong Zeng, Ilyas Chahed, Maksim Velikanov, Dhia Eddine Rhaiem, Pasquale Balsebre, Abhay Kumar, Younes Belkada, Hakim Hacid

llms

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

Sep 1

Training-free LLM Verification via Recycling Few-shot Examples

arXiv:2506.17251v3 Announce Type: replace-cross Abstract: Although large language models (LLMs) have achieved remarkable performance, the inherent stochasticity of their reasoning processes and varyi...

By Dongseok Lee, Jimyung Hong, Dongyoung Kim, Jaehyung Kim

llms

arXiv AI

6d ago

LLM Agents for Automated Post-Training via Structured Recipe Optimization

arXiv:2610.18572v1 Announce Type: new Abstract: Automated post-training transforms pre-trained large language models (LLMs) into models capable of handling diverse domains by automatically constructi...

By Ruohong Liu, Bohan Tang, Lekang Jiang, Jack Umenberger, Yiwen Guo

llms agents benchmarks

arXiv Machine Learning

Jul 21

Breaking the Block: Preserving Data Continuity to Train Superior SAEs for Instruct Models

arXiv:2506. 07691v2 Announce Type: replace-cross Abstract: Sparse Autoencoders (SAEs) are a cornerstone of mechanistic interpretability.

By Jiaming Li, Haoran Ye, Yukun Chen, Xinyue Li, Lei Zhang, Hamid Alinejad-Rokny, Jimmy Chih-Hsien Peng, Min Yang

llms fine-tuning safety

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