5 papers · 1 filter
Dripper: Token-Efficient Main HTML Extraction with a Lightweight LM
Mengjie Liu, Jiahui Peng, Wenchang Ning +14
High-quality main content extraction from web pages is a critical prerequisite for constructing large-scale training corpora. While traditional heuristic extractors are efficient,…
AICC: Parse HTML Finer, Make Models Better -- A 7.3T AI-Ready Corpus Built by a Model-Based HTML Parser
Ren Ma, Jiantao Qiu, Chao Xu +26
While web data quality is crucial for large language models, most curation efforts focus on filtering and deduplication,treating HTML-to-text extraction as a fixed pre-processing s…
Topic Over Source: The Key to Effective Data Mixing for Language Models Pre-training
Jiahui Peng, Xinlin Zhuang, Jiantao Qiu +4
The performance of large language models (LLMs) is significantly affected by the quality and composition of their pre-training data, which is inherently diverse, spanning various l…
Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models
Xinlin Zhuang, Jiahui Peng, Ren Ma +7
The composition of pre-training datasets for large language models (LLMs) remains largely undisclosed, hindering transparency and efforts to optimize data quality, a critical drive…
Efficient Pretraining Data Selection for Language Models via Multi-Actor Collaboration
Tianyi Bai, Ling Yang, Zhen Hao Wong +9
Efficient data selection is crucial to accelerate the pretraining of language model (LMs). While various methods have been proposed to enhance data efficiency, limited research has…