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Traditionally, these NLP techniques have relied on labor-intensive and potentially error-prone manual annotation processes to build the corpora necessary to train them. This article explores and evaluates the use of\u00a0Large Language Models (LLMs) as an alternative for effectively and efficiently identifying and categorizing a variety of data practice disclosures found\u00a0in the text of privacy policies. Specifically, we report on the performance of ChatGPT and Llama 2,\u00a0two particularly popular LLM-based tools. This includes engineering prompts and evaluating different configurations of these LLM techniques.\u00a0Evaluation of the resulting techniques on well-known corpora of privacy policy annotations yields an\u00a0F1 score exceeding 93%. This score is higher than scores reported earlier in the literature on these\u00a0benchmarks. This performance is obtained at\u00a0minimal marginal\u00a0cost (excluding the cost required to train the foundational models themselves). These results, which are consistent with those reported in other domains, suggest that LLMs offer a particularly promising approach to automated privacy policy analysis at scale.<\/jats:p>","DOI":"10.1007\/s00607-024-01331-9","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T17:43:40Z","timestamp":1724435020000},"page":"3879-3903","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["Large language models: a new approach for privacy policy analysis at scale"],"prefix":"10.1007","volume":"106","author":[{"given":"David","family":"Rodriguez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose M.","family":"Del Alamo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norman","family":"Sadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"1331_CR1","doi-asserted-by":"publisher","unstructured":"Srinath M, Matheson L, Venkit PN, Zanfir-Fortuna G, Schaub F, Giles CL, Wilson S (2023) Privacy now or never: Large-scale extraction and analysis of dates in privacy policy text. 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