Herbarium specimen label transcription reimagined with large language models: capabilities, productivity, and risks.

Natural history collections balance immense scientific value with financial constraints. Despite their key role in biodiversity research, they often face resource and staffing shortages. Within this complex landscape, the rise of large language models (LLMs; see Table 1 for definitions of italicized terms), particularly ChatGPT, is notable. LLMs have quickly gained prominence, garnering praise as innovative tools while also facing criticism for biases, potential misuse, data privacy, and data ownership concerns. This article is protected by copyright. All rights reserved.