March 19, 2024, 4:54 a.m. | Junyuan Hong, Jiachen T. Wang, Chenhui Zhang, Zhangheng Li, Bo Li, Zhangyang Wang

cs.CL updates on arXiv.org arxiv.org

arXiv:2312.03724v2 Announce Type: replace
Abstract: Large Language Models (LLMs) have emerged as dominant tools for various tasks, particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns surrounding data privacy present obstacles due to the tuned prompts' dependency on sensitive private information. A practical solution is to host a local LLM and optimize a soft prompt privately using data. Yet, hosting a local model becomes problematic when model ownership is protected. Alternative methods, like sending data to the …

arxiv cs.ai cs.cl engineer language language model large language large language model privacy prompt prompt engineer type

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