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Paper · 2502.08265 · 2025

Exploring the Potential of Large Language Models to Simulate Personality

arXiv · PDF · Open in the Atlas

Code that ran

We lifted 6 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.

RepositoryRoleRan
mary-silence/simulating_personality canonical 6 of 6
FunctionStatusWhere it lives
adjusted_score Ran mary-silence/simulating_personality/experiment_functions/questionnaire.py
code served (permissive licence) · get_code("535972bb92f04305")
get_llm_answers Ran mary-silence/simulating_personality/experiment_functions/qa_text_generation.py
code served (permissive licence) · get_code("f0cb9a95e3270d01")
get_llm_answers Ran mary-silence/simulating_personality/experiment_functions/questionnaire.py
code served (permissive licence) · get_code("e0a1124343f9fe5b")
load_data Ran mary-silence/simulating_personality/experiment_functions/qa_text_generation.py
code served (permissive licence) · get_code("7bc1b7dc7e5a310f")
map_trait_score Ran mary-silence/simulating_personality/experiment_functions/qa_text_generation.py
code served (permissive licence) · get_code("11f0a87cb42f413a")
match_score Ran mary-silence/simulating_personality/experiment_functions/questionnaire.py
code served (permissive licence) · get_code("10fc91019ecc483f")

Repositories linked to this paper

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Abstract

With the advancement of large language models (LLMs), the focus in Conversational AI has shifted from merely generating coherent and relevant responses to tackling more complex challenges, such as personalizing dialogue systems. In an effort to enhance user engagement, chatbots are often designed to mimic human behaviour, responding within a defined emotional spectrum and aligning to a set of values. In this paper, we aim to simulate personal traits according to the Big Five model with the use of LLMs. Our research showed that generating personality-related texts is still a challenging task for the models. As a result, we present a dataset of generated texts with the predefined Big Five characteristics and provide an analytical framework for testing LLMs on a simulation of personality skills.

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