Study: Transparency is Often Lacking in Datasets Used to Train Large Language Models

Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.

Bibliographic Details
Main Author: Zewe, Adam
Format: Article
Published: MIT News 2024
Subjects:
Online Access:https://hdl.handle.net/1721.1/157453
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author Zewe, Adam
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author_sort Zewe, Adam
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description Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.
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spelling mit-1721.1/1574532024-11-01T03:43:04Z Study: Transparency is Often Lacking in Datasets Used to Train Large Language Models Zewe, Adam LLSC AI Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias. 2024-10-31T13:05:02Z 2024-10-31T13:05:02Z 2024-08-30 Article https://hdl.handle.net/1721.1/157453 application/pdf MIT News
spellingShingle LLSC
AI
Zewe, Adam
Study: Transparency is Often Lacking in Datasets Used to Train Large Language Models
title Study: Transparency is Often Lacking in Datasets Used to Train Large Language Models
title_full Study: Transparency is Often Lacking in Datasets Used to Train Large Language Models
title_fullStr Study: Transparency is Often Lacking in Datasets Used to Train Large Language Models
title_full_unstemmed Study: Transparency is Often Lacking in Datasets Used to Train Large Language Models
title_short Study: Transparency is Often Lacking in Datasets Used to Train Large Language Models
title_sort study transparency is often lacking in datasets used to train large language models
topic LLSC
AI
url https://hdl.handle.net/1721.1/157453
work_keys_str_mv AT zeweadam studytransparencyisoftenlackingindatasetsusedtotrainlargelanguagemodels