口语医学对话系统越来越引起人们的兴趣，以增强获得医疗服务的机会并提高患者护理的质量和可追溯性。在本文中，我们专注于通过口语对话在智能手机上获得的医疗药物处方。这样的系统将促进护理的可追溯性，并可以释放临床医生的时间。但是，由于大多数相关语料库都是文本形式和英语，因此缺乏语音语料库来开发此类系统。为了促进口头医学对话系统的研究和开发，据我们所知，我们介绍了第一个名为PXSLU的口语医学药物处方语料库。它包含通过与55名参与者专家的实验获得的法国药物处方的4小时和注释对话，并在处方中进行了非专家。我们还提出了一些实验，这些实验证明了该语料库对医学对话系统的评估和开发的兴趣。
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Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License.
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加泰罗坦语言理解基准（Club）包括代表不同NLU任务的各种数据集，以便在一般语言理解评估（胶水）示例之后，可以准确评估语言模型。它是Aina和Plantl的一部分，两项公共资金举措，以赋予人工智能时代的加泰罗尼亚语言。
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