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Xingyu Shen

2 accepted papers

2025

AutoClean: LLMs Can Prepare Their Training Corpus

NAACL 2025system demonstrations

Recent studies highlight the reliance of Large Language Models (LLMs) on high-quality, diverse data for optimal performance. The data sourced from the Internet often aggregated into datasets like the Common Crawl corpus, presents significant quality variability and necessitates extensive cleaning. M…

Cited by 0SourcePDFScholar
2025

Cost-Optimal Grouped-Query Attention for Long-Context Modeling

EMNLP 2025

Grouped-Query Attention (GQA) is a widely adopted strategy for reducing the computational cost of attention layers in large language models (LLMs). However, current GQA configurations are often suboptimal because they overlook how context length influences inference cost. Since inference cost grows