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LLM Implications- one-pager
dc.contributor.authorOkerlund, Johanna
dc.contributor.authorKlasky, Evan
dc.contributor.authorMiddha, Aditya
dc.contributor.authorKim, Sujin
dc.contributor.authorRosenfeld, Hannah
dc.contributor.authorKleinman, Molly
dc.contributor.authorParthasarathy, Shobita
dc.date.accessioned2023-12-14T17:48:52Z
dc.date.available2023-12-14T17:48:52Z
dc.date.issued2022-04
dc.identifier.urihttps://hdl.handle.net/2027.42/191727en
dc.description.abstractLLMs have tremendous potential to empower communities and democratize knowledge. But given the concentrated development landscape and the datasets on which they are based, LLMs are unlikely to achieve these goals.en_US
dc.description.sponsorshipAlfred P. Sloan Foundation (grant #G-2021-16769)en_US
dc.language.isoen_USen_US
dc.subjectLLM, large language models, ChatGPT,en_US
dc.titleLLM Implications- one-pageren_US
dc.typeTechnical Reporten_US
dc.subject.hlbtoplevelGovernment, Politics and Law
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/191727/1/large-language-models-one-pager STPP-TAP-2022-v3.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/191727/2/UM TAP Large Language Models Executive Summary 2022.pdf
dc.identifier.doihttps://dx.doi.org/10.7302/21907
dc.description.filedescriptionDescription of large-language-models-one-pager STPP-TAP-2022-v3.pdf : one-pager
dc.description.filedescriptionDescription of UM TAP Large Language Models Executive Summary 2022.pdf : Executive Summary
dc.description.depositorSELFen_US
dc.working.doi10.7302/21907en_US
dc.owningcollnameScience, Technology, and Public Policy (STPP) program


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