商业大型语言模型在非洲语言上的表现如何？
How Good are Commercial Large Language Models on African Languages?
AI总结：
本研究初步评估商业大型语言模型在八种非洲语言的机器翻译和文本分类任务上的表现，发现其性能低于标准，且文本分类优于机器翻译，呼吁关注非洲语言的代表性。
摘要：
自然语言处理（NLP）的最新进展导致了大型预训练语言模型的激增。这些模型已被证明，通过上下文学习，即使在未见过的任务和语言上也能产生良好的性能。它们也以语言模型即服务的形式作为商业API被广泛采用。然而，它们在非洲语言上的表现很大程度上是未知的。我们对商业大型语言模型在八种非洲语言上的两个任务（机器翻译和文本分类）进行了初步分析，这些语言跨越不同的语系和地理区域。我们的结果表明，商业语言模型在非洲语言上产生低于标准的表现。我们还发现，它们在文本分类上的表现优于机器翻译。总的来说，鉴于商业大型语言模型日益增长的普及性，我们的发现呼吁采取行动，确保非洲语言在这些模型中得到充分体现。
英文摘要：
Recent advancements in Natural Language Processing (NLP) has led to the proliferation of large pretrained language models. These models have been shown to yield good performance, using in-context learning, even on unseen tasks and languages. They have also been exposed as commercial APIs as a form of language-model-as-a-service, with great adoption. However, their performance on African languages is largely unknown. We present a preliminary analysis of commercial large language models on two tasks (machine translation and text classification) across eight African languages, spanning different language families and geographical areas. Our results suggest that commercial language models produce below-par performance on African languages. We also find that they perform better on text classification than machine translation. In general, our findings present a call-to-action to ensure African languages are well represented in commercial large language models, given their growing popularity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
