Know Thyself, Improve Thyself: Personalized LLMs for Self-Knowledge and Moral Enhancement
In this paper, we suggest that personalized LLMs trained on information written by or otherwise pertaining to an individual could serve as artificial moral advisors (AMAs) that account for the dynamic nature of personal morality. These LLM-based AMAs would harness users’ past and present data to inf...
Main Authors: | , , , , |
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Format: | Journal article |
Language: | English |
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Springer
2024
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_version_ | 1817931549514924032 |
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author | Giubilini, A Porsdam Mann, S Voinea, C Earp, B Savulescu, J |
author_facet | Giubilini, A Porsdam Mann, S Voinea, C Earp, B Savulescu, J |
author_sort | Giubilini, A |
collection | OXFORD |
description | In this paper, we suggest that personalized LLMs trained on information written by or otherwise pertaining to an individual could serve as artificial moral advisors (AMAs) that account for the dynamic nature of personal morality. These LLM-based AMAs would harness users’ past and present data to infer and make explicit their sometimes-shifting values and preferences, thereby fostering self-knowledge. Further, these systems may also assist in processes of self-creation, by helping users reflect on the kind of person they want to be and the actions and goals necessary for so becoming. The feasibility of LLMs providing such personalized moral insights remains uncertain pending further technical development. Nevertheless, we argue that this approach addresses limitations in existing AMA proposals reliant on either predetermined values or introspective self-knowledge. |
first_indexed | 2024-12-09T03:23:47Z |
format | Journal article |
id | oxford-uuid:55d4848d-4fe7-4777-8727-166cf989332f |
institution | University of Oxford |
language | English |
last_indexed | 2024-12-09T03:23:47Z |
publishDate | 2024 |
publisher | Springer |
record_format | dspace |
spelling | oxford-uuid:55d4848d-4fe7-4777-8727-166cf989332f2024-11-21T20:09:56ZKnow Thyself, Improve Thyself: Personalized LLMs for Self-Knowledge and Moral EnhancementJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:55d4848d-4fe7-4777-8727-166cf989332fEnglishJisc Publications RouterSpringer2024Giubilini, APorsdam Mann, SVoinea, CEarp, BSavulescu, JIn this paper, we suggest that personalized LLMs trained on information written by or otherwise pertaining to an individual could serve as artificial moral advisors (AMAs) that account for the dynamic nature of personal morality. These LLM-based AMAs would harness users’ past and present data to infer and make explicit their sometimes-shifting values and preferences, thereby fostering self-knowledge. Further, these systems may also assist in processes of self-creation, by helping users reflect on the kind of person they want to be and the actions and goals necessary for so becoming. The feasibility of LLMs providing such personalized moral insights remains uncertain pending further technical development. Nevertheless, we argue that this approach addresses limitations in existing AMA proposals reliant on either predetermined values or introspective self-knowledge. |
spellingShingle | Giubilini, A Porsdam Mann, S Voinea, C Earp, B Savulescu, J Know Thyself, Improve Thyself: Personalized LLMs for Self-Knowledge and Moral Enhancement |
title | Know Thyself, Improve Thyself: Personalized LLMs for Self-Knowledge and Moral Enhancement |
title_full | Know Thyself, Improve Thyself: Personalized LLMs for Self-Knowledge and Moral Enhancement |
title_fullStr | Know Thyself, Improve Thyself: Personalized LLMs for Self-Knowledge and Moral Enhancement |
title_full_unstemmed | Know Thyself, Improve Thyself: Personalized LLMs for Self-Knowledge and Moral Enhancement |
title_short | Know Thyself, Improve Thyself: Personalized LLMs for Self-Knowledge and Moral Enhancement |
title_sort | know thyself improve thyself personalized llms for self knowledge and moral enhancement |
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