Not Quite 'Ask a Librarian': AI on the Nature, Value, and Future of LIS
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Abstract (arXiv)
AI language models trained on Web data generate prose that reflects human knowledge and public sentiments, but can also contain novel insights and predictions. We asked the world's best language model, GPT-3, fifteen difficult questions about the nature, value, and future of library and information science (LIS), topics that receive perennial attention from LIS scholars. We present highlights from its 45 different responses, which range from platitudes and caricatures to interesting perspectives and worrisome visions of the future, thus providing an LIS-tailored demonstration of the current performance of AI language models. We also reflect on the viability of using AI to forecast or generate research ideas in this way today. Finally, we have shared the full response log online for readers to consider and evaluate for themselves.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 44 references that are in the map. First posted on arXiv 7 Jul 2021.
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6 of 44 references.
References · 6
- 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
- 2021Persistent Anti-Muslim Bias in Large Language Modelscited by 377
- 2019Artificial Intelligence: the global landscape of ethics guidelinescited by 107
- 2020Artificial Intelligence in the Creative Industries: A Reviewcited by 61
- 2020Transparency in Language Generation: Levels of Automationcited by 2
- 2020The State of AI Ethics Report (October 2020)cited by 1
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