most citedFinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation

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cs.LG2026

WIN-U: Woodbury-Informed Newton-Unlearning as a retain-free Machine Unlearning Framework

Xingjian Zhao, Mohammad Mohammadi Amiri, Malik Magdon-Ismail

Privacy concerns in LLMs have led to the rapidly growing need to enforce a data's "right to be forgotten". Machine unlearning addresses precisely this task, namely the removal of t…

cs.LG2025★ 2 cited

FinLoRA: Finetuning Quantized Financial Large Language Models Using Low-Rank Adaptation

Dannong Wang, Daniel Kim, Bo Jin +4

Finetuned large language models (LLMs) have shown remarkable performance in financial tasks, such as sentiment analysis and information retrieval. Due to privacy concerns, finetuni…