Study on Anti-fraud Information Identification Method Based on Fine-tuning Techniques of Large Language Models
Aiming at the anti-fraud information identification,it conductes in-depth experimental research on fine-tuning techniques of large language models(LLMs).It selectes three LLMs base models of different scales and employes two advanced fine-tuning technologies,LoRA and p-tuning v2,to adapt to specific anti-fraud information identification tasks.Through experimental evaluations across multiple dimensions,fine-tuning strategies not only significantly enhances the models'performance in anti-fraud information identification,but also maintains the universality of the model to a certain extent.Additionally,it explores the learning capabilities of LLMs under low-sample conditions and analyzes the resource consumption under different fine-tuning strategies.
LLMsFine-tuning techniquesAnti-fraud information identificationLoRAp-tuning v2Few-shot learning
NETL
NSTL
万方数据
