2022
Authors
Gonçalves, T; Torto, IR; Teixeira, LF; Cardoso, JS;
Publication
CoRR
Abstract
2022
Authors
Torto, IR; Patrício, C; Montenegro, H; Gonçalves, T;
Publication
Proceedings of the Working Notes of CLEF 2022 - Conference and Labs of the Evaluation Forum, Bologna, Italy, September 5th - to - 8th, 2022.
Abstract
2023
Authors
Dani, M; Torto, IR; Alaniz, S; Akata, Z;
Publication
CoRR
Abstract
2023
Authors
Salewski, L; Alaniz, S; Torto, IR; Schulz, E; Akata, Z;
Publication
CoRR
Abstract
2023
Authors
Salewski, L; Alaniz, S; Rio-Torto, I; Schulz, E; Akata, Z;
Publication
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 36 (NEURIPS 2023)
Abstract
In everyday conversations, humans can take on different roles and adapt their vocabulary to their chosen roles. We explore whether LLMs can take on, that is impersonate, different roles when they generate text in-context. We ask LLMs to assume different personas before solving vision and language tasks. We do this by prefixing the prompt with a persona that is associated either with a social identity or domain expertise. In a multi-armed bandit task, we find that LLMs pretending to be children of different ages recover human-like developmental stages of exploration. In a language-based reasoning task, we find that LLMs impersonating domain experts perform better than LLMs impersonating non-domain experts. Finally, we test whether LLMs' impersonations are complementary to visual information when describing different categories. We find that impersonation can improve performance: an LLM prompted to be a bird expert describes birds better than one prompted to be a car expert. However, impersonation can also uncover LLMs' biases: an LLM prompted to be a man describes cars better than one prompted to be a woman. These findings demonstrate that LLMs are capable of taking on diverse roles and that this in-context impersonation can be used to uncover their strengths and hidden biases. Our code is available at https://github.com/ExplainableML/in-context-impersonation.
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