title: Personality Traits in Large Language Models
summary: 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.
paper_authors._id: 64a38175fbd6fdd06d7c38fc
paper_authors.name: Mustafa Safdari
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-04T19:17:05.967Z
paper_authors._id: 6430b08b74e7df5793edf353
paper_authors.avatarUrl: /avatars/67875a201ffc22416acbf74b759b544b.svg
paper_authors.fullname: Mustafa Safdari
paper_authors.type: user
paper_authors.user: TheMegusAlexandrus
paper_authors._id: 64a38175fbd6fdd06d7c38fd
paper_authors.name: Greg Serapio-García
paper_authors._id: 64a38175fbd6fdd06d7c38fe
paper_authors.name: Clément Crepy
paper_authors._id: 64a38175fbd6fdd06d7c38ff
paper_authors.name: Stephen Fitz
paper_authors._id: 64a38175fbd6fdd06d7c3900
paper_authors.name: Peter Romero
paper_authors._id: 64a38175fbd6fdd06d7c3901
paper_authors.name: Luning Sun
paper_authors._id: 64a38175fbd6fdd06d7c3902
paper_authors.name: Marwa Abdulhai
paper_authors._id: 64a38175fbd6fdd06d7c3903
paper_authors.name: Aleksandra Faust
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-04T07:25:06.633Z
paper_authors._id: 64a3b828225b2121f6b3beb3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64a3b828225b2121f6b3beb3/44AgAQENc7cJMOWcoK4g_.jpeg
paper_authors.fullname: Aleksandra Faust
paper_authors.type: user
paper_authors.user: sandraorion
paper_authors._id: 64a38175fbd6fdd06d7c3904
paper_authors.name: Maja Matarić
paper_title: Personality Traits in Large Language Models
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) zeroth-order processing synthesized personality human-generated data psychometric tests personality traits prompting configurations reliability validity instruction fine-tuned models specific personality profiles responsible use of LLMs