1 citations · 2 across the 10 of their papers we have counts for
4 papers · 1 filter
Reclaiming Low-Quality Instruction Data with Rule-Guided LLM Reconstruction
Zile Yang, Ling Li, Na Di +5
Data selection for supervised fine-tuning (SFT) often discards low-scoring instruction examples that may retain useful supervision. We propose a rule-guided LLM reconstruction fram…
Incentivizing High-quality Participation From Federated Learning Agents
Jinlong Pang, Jiaheng Wei, Yifan Hua +2
Federated learning (FL) provides a promising paradigm for facilitating collaboration between multiple clients that jointly learn a global model without directly sharing their local…
Evaluating LLM-Contaminated Crowdsourcing Data Without Ground Truth
Yichi Zhang, Jinlong Pang, Zhaowei Zhu +1
The recent success of generative AI highlights the crucial role of high-quality human feedback in building trustworthy AI systems. However, the increasing use of large language mod…
Token Cleaning: Fine-Grained Data Selection for LLM Supervised Fine-Tuning
Jinlong Pang, Na Di, Zhaowei Zhu +4
Recent studies show that in supervised fine-tuning (SFT) of large language models (LLMs), data quality matters more than quantity. While most data cleaning methods concentrate on f…