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cs.CL2025
Finetune-RAG: Fine-Tuning Language Models to Resist Hallucination in Retrieval-Augmented Generation
Zhan Peng Lee, Andre Lin, Calvin Tan
Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to improve factuality in large language models (LLMs) by grounding their outputs in retrieved documents. Ho…
cs.CL2024★ 1 cited
1.5-Pints Technical Report: Pretraining in Days, Not Months -- Your Language Model Thrives on Quality Data
Calvin Tan, Jerome Wang
This paper presents a compute-efficient approach to pre-training a Language Model-the "1.5-Pints"-in only 9 days, while outperforming state-of-the-art models as an instruction-foll…