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...

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Main Authors: Giubilini, A, Porsdam Mann, S, Voinea, C, Earp, B, Savulescu, J
Format: Journal article
Language:English
Published: Springer 2024
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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.
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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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