AI RESEARCH
Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
arXiv CS.AI
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ArXi:2502.01968v3 Announce Type: replace-cross Recent studies show that in supervised fine-tuning (SFT) of large language models (LLMs), data quality matters than quantity. While most data cleaning methods concentrate on filtering entire samples, the quality of individual tokens within a sample can vary significantly. After pre-