Personality Traits in Large Language Models
The advent of large language models (LLMs) has revolutionized natural
language processing, enabling the generation of coherent and contextually
relevant text. As LLMs increasingly power conversational agents, the
synthesized personality embedded in these models by virtue of their training on
large amounts of human-generated data draws attention. Since personality is an
important factor determining the effectiveness of communication, we present a
comprehensive method for administering validated psychometric tests and
quantifying, analyzing, and shaping personality traits exhibited in text
generated from widely-used LLMs. We find that: 1) personality simulated in the
outputs of some LLMs (under specific prompting configurations) is reliable and
valid; 2) evidence of reliability and validity of LLM-simulated personality is
stronger for larger and instruction fine-tuned models; and 3) personality in
LLM outputs can be shaped along desired dimensions to mimic specific
personality profiles. We also discuss potential applications and ethical
implications of our measurement and shaping framework, especially regarding
responsible use of LLMs.
64a38175fbd6fdd06d7c38fc
Mustafa Safdari
claimed_verified
2023-07-04T19:17:05.967Z
6430b08b74e7df5793edf353
/avatars/67875a201ffc22416acbf74b759b544b.svg
Mustafa Safdari
user
TheMegusAlexandrus
64a38175fbd6fdd06d7c38fd
Greg Serapio-García
64a38175fbd6fdd06d7c38fe
Clément Crepy
64a38175fbd6fdd06d7c38ff
Stephen Fitz
64a38175fbd6fdd06d7c3900
Peter Romero
64a38175fbd6fdd06d7c3901
Luning Sun
64a38175fbd6fdd06d7c3902
Marwa Abdulhai
64a38175fbd6fdd06d7c3903
Aleksandra Faust
claimed_verified
2023-07-04T07:25:06.633Z
64a3b828225b2121f6b3beb3
https://cdn-avatars.huggingface.co/v1/production/uploads/64a3b828225b2121f6b3beb3/44AgAQENc7cJMOWcoK4g_.jpeg
Aleksandra Faust
user
sandraorion
64a38175fbd6fdd06d7c3904
Maja Matarić
Personality Traits in Large Language Models
The advent of large language models (LLMs) has revolutionized natural
language processing, enabling the generation of coherent and contextually
relevant text. As LLMs increasingly power conversational agents, the
synthesized personality embedded in these models by virtue of their training on
large amounts of human-generated data draws attention. Since personality is an
important factor determining the effectiveness of communication, we present a
comprehensive method for administering validated psychometric tests and
quantifying, analyzing, and shaping personality traits exhibited in text
generated from widely-used LLMs. We find that: 1) personality simulated in the
outputs of some LLMs (under specific prompting configurations) is reliable and
valid; 2) evidence of reliability and validity of LLM-simulated personality is
stronger for larger and instruction fine-tuned models; and 3) personality in
LLM outputs can be shaped along desired dimensions to mimic specific
personality profiles. We also discuss potential applications and ethical
implications of our measurement and shaping framework, especially regarding
responsible use of LLMs.
A method quantifies and manipulates personality traits in large language models' outputs, finding reliability and validity enhanced in larger, instruction-fine-tuned models and exploring ethical considerations.
large language models LLMs psychometric tests personality traits instruction fine-tuned models ethical implications