Kory Wallace Mathewson

Shakir Mohamed

2024-06-05

The 2024 ACM Conference on Fairness, Accountability, and Transparency (publié)

A Robot Walks into a Bar: Can Language Models Serve as Creativity SupportTools for Comedy? An Evaluation of LLMs’ Humour Alignment with Comedians

Piotr Mirowski

Juliette Love

Shakir Mohamed

We interviewed twenty professional comedians who perform live shows in front of audiences and who use artificial intelligence in their artis… (voir plus)tic process as part of 3-hour workshops on “AI x Comedy” conducted at the Edinburgh Festival Fringe in August 2023 and online. The workshop consisted of a comedy writing session with large language models (LLMs), a human-computer interaction questionnaire to assess the Creativity Support Index of AI as a writing tool, and a focus group interrogating the comedians’ motivations for and processes of using AI, as well as their ethical concerns about bias, censorship and copyright. Participants noted that existing moderation strategies used in safety filtering and instruction-tuned LLMs reinforced hegemonic viewpoints by erasing minority groups and their perspectives, and qualified this as a form of censorship. At the same time, most participants felt the LLMs did not succeed as a creativity support tool, by producing bland and biased comedy tropes, akin to “cruise ship comedy material from the 1950s, but a bit less racist”. Our work extends scholarship about the subtle difference between, one the one hand, harmful speech, and on the other hand, “offensive” language as a practice of resistance, satire and “punching up”. We also interrogate the global value alignment behind such language models, and discuss the importance of community-based value alignment and data ownership to build AI tools that better suit artists’ needs. Warning: this study may contain offensive language and discusses self-harm.

2024-05-31

ArXiv (prépublication)

A Robot Walks into a Bar: Can Language Models Serve as Creativity SupportTools for Comedy? An Evaluation of LLMs’ Humour Alignment with Comedians

Piotr Mirowski

Juliette Love

Shakir Mohamed

We interviewed twenty professional comedians who perform live shows in front of audiences and who use artificial intelligence in their artis… (voir plus)tic process as part of 3-hour workshops on “AI x Comedy” conducted at the Edinburgh Festival Fringe in August 2023 and online. The workshop consisted of a comedy writing session with large language models (LLMs), a human-computer interaction questionnaire to assess the Creativity Support Index of AI as a writing tool, and a focus group interrogating the comedians’ motivations for and processes of using AI, as well as their ethical concerns about bias, censorship and copyright. Participants noted that existing moderation strategies used in safety filtering and instruction-tuned LLMs reinforced hegemonic viewpoints by erasing minority groups and their perspectives, and qualified this as a form of censorship. At the same time, most participants felt the LLMs did not succeed as a creativity support tool, by producing bland and biased comedy tropes, akin to “cruise ship comedy material from the 1950s, but a bit less racist”. Our work extends scholarship about the subtle difference between, one the one hand, harmful speech, and on the other hand, “offensive” language as a practice of resistance, satire and “punching up”. We also interrogate the global value alignment behind such language models, and discuss the importance of community-based value alignment and data ownership to build AI tools that better suit artists’ needs. Warning: this study may contain offensive language and discusses self-harm.

2024-05-31

ArXiv (prépublication)

Divergent Creativity in Humans and Large Language Models

Antoine Bellemare-Pepin

Franccois Lespinasse

Yann Harel

Jay A. Olson

Karim Jerbi CoCo Lab

Psychology Department

U. Montr'eal

Montreal

Qc

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (voir 5 de plus)

Mississauga

On

Department of Computer Science

Operations Research

Unique Center

The recent surge in the capabilities of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin … (voir plus)to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLM creativity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in creativity science to build a framework for in-depth analysis of divergent creativity in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence suggesting that LLMs can indeed surpass human capabilities in specific creative tasks such as divergent association and creative writing. Our quantitative benchmarking framework opens up new paths for the development of more creative LLMs, but it also encourages more granular inquiries into the distinctive elements that constitute human inventive thought processes, compared to those that can be artificially generated.

2024-05-13

ArXiv (prépublication)

Divergent Creativity in Humans and Large Language Models

Antoine Bellemare‐Pepin

Franccois Lespinasse

Yann Harel

Jay A. Olson

Karim Jerbi CoCo Lab

Psychology Department

U. Montr'eal

Montreal

Qc

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (voir 5 de plus)

Mississauga

On

Department of Computer Science

Operations Research

Unique Center

The recent surge in the capabilities of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin … (voir plus)to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLM creativity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in creativity science to build a framework for in-depth analysis of divergent creativity in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence suggesting that LLMs can indeed surpass human capabilities in specific creative tasks such as divergent association and creative writing. Our quantitative benchmarking framework opens up new paths for the development of more creative LLMs, but it also encourages more granular inquiries into the distinctive elements that constitute human inventive thought processes, compared to those that can be artificially generated.

2024-05-13

ArXiv (prépublication)

Divergent Creativity in Humans and Large Language Models

Antoine Bellemare-Pepin

Franccois Lespinasse

Yann Harel

Jay A. Olson

Karim Jerbi CoCo Lab

Psychology Department

U. Montr'eal

Montreal

Qc

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (voir 5 de plus)

Mississauga

On

Department of Computer Science

Operations Research

Unique Center

The recent surge in the capabilities of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin … (voir plus)to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLM creativity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in creativity science to build a framework for in-depth analysis of divergent creativity in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence suggesting that LLMs can indeed surpass human capabilities in specific creative tasks such as divergent association and creative writing. Our quantitative benchmarking framework opens up new paths for the development of more creative LLMs, but it also encourages more granular inquiries into the distinctive elements that constitute human inventive thought processes, compared to those that can be artificially generated.

2024-05-13

ArXiv (prépublication)

Divergent Creativity in Humans and Large Language Models

Antoine Bellemare‐Pepin

Franccois Lespinasse

Yann Harel

Jay A. Olson

Karim Jerbi CoCo Lab

Psychology Department

U. Montr'eal

Montreal.

Qc

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (voir 5 de plus)

Mississauga

On

Department of Computer Science

Operations Research

Unique Center

The recent surge of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilitie… (voir plus)s. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLMs'semantic diversity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in computational creativity to analyze semantic divergence in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence that LLMs can surpass average human performance on the Divergent Association Task, and approach human creative writing abilities, though they fall short of the typical performance of highly creative humans. Notably, even the top performing LLMs are still largely surpassed by highly creative individuals, underscoring a ceiling that current LLMs still fail to surpass. Our human-machine benchmarking framework addresses the polemic surrounding the imminent replacement of human creative labour by AI, disentangling the quality of the respective creative linguistic outputs using established objective measures. While prompting deeper exploration of the distinctive elements of human inventive thought compared to those of AI systems, we lay out a series of techniques to improve their outputs with respect to semantic diversity, such as prompt design and hyper-parameter tuning.

2024-05-13

ArXiv (prépublication)

Divergent Creativity in Humans and Large Language Models

Antoine Bellemare‐Pepin

Franccois Lespinasse

Yann Harel

Jay A. Olson

Karim Jerbi CoCo Lab

Psychology Department

U. Montr'eal

Montreal.

Qc

Canada

Music department

C. University

Sociology

Anthropology department

Mila

Departmentof Psychology

University of Toronto Mississauga … (voir 5 de plus)

Mississauga

On

Department of Computer Science

Operations Research

Unique Center

The recent surge of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilitie… (voir plus)s. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLMs'semantic diversity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in computational creativity to analyze semantic divergence in both state-of-the-art LLMs and a substantial dataset of 100,000 humans. We found evidence that LLMs can surpass average human performance on the Divergent Association Task, and approach human creative writing abilities, though they fall short of the typical performance of highly creative humans. Notably, even the top performing LLMs are still largely surpassed by highly creative individuals, underscoring a ceiling that current LLMs still fail to surpass. Our human-machine benchmarking framework addresses the polemic surrounding the imminent replacement of human creative labour by AI, disentangling the quality of the respective creative linguistic outputs using established objective measures. While prompting deeper exploration of the distinctive elements of human inventive thought compared to those of AI systems, we lay out a series of techniques to improve their outputs with respect to semantic diversity, such as prompt design and hyper-parameter tuning.

2024-05-13

ArXiv (prépublication)

Designing and Evaluating Dialogue LLMs for Co-Creative Improvised Theatre

Boyd Branch

Piotr Mirowski

Sophia Ppali

Alexandra Covaci

Social robotics researchers are increasingly interested in multi-party trained conversational agents. With a growing demand for real-world e… (voir plus)valuations, our study presents Large Language Models (LLMs) deployed in a month-long live show at the Edinburgh Festival Fringe. This case study investigates human improvisers co-creating with conversational agents in a professional theatre setting. We explore the technical capabilities and constraints of on-the-spot multi-party dialogue, providing comprehensive insights from both audience and performer experiences with AI on stage. Our human-in-the-loop methodology underlines the challenges of these LLMs in generating context-relevant responses, stressing the user interface's crucial role. Audience feedback indicates an evolving interest for AI-driven live entertainment, direct human-AI interaction, and a diverse range of expectations about AI's conversational competence and utility as a creativity support tool. Human performers express immense enthusiasm, varied satisfaction, and the evolving public opinion highlights mixed emotions about AI's role in arts.

2024-05-11

ArXiv (prépublication)

Designing and Evaluating Dialogue LLMs for Co-Creative Improvised Theatre

Boyd Branch

Piotr Mirowski

Sophia Ppali

Alexandra Covaci

Social robotics researchers are increasingly interested in multi-party trained conversational agents. With a growing demand for real-world e… (voir plus)valuations, our study presents Large Language Models (LLMs) deployed in a month-long live show at the Edinburgh Festival Fringe. This case study investigates human improvisers co-creating with conversational agents in a professional theatre setting. We explore the technical capabilities and constraints of on-the-spot multi-party dialogue, providing comprehensive insights from both audience and performer experiences with AI on stage. Our human-in-the-loop methodology underlines the challenges of these LLMs in generating context-relevant responses, stressing the user interface's crucial role. Audience feedback indicates an evolving interest for AI-driven live entertainment, direct human-AI interaction, and a diverse range of expectations about AI's conversational competence and utility as a creativity support tool. Human performers express immense enthusiasm, varied satisfaction, and the evolving public opinion highlights mixed emotions about AI's role in arts.

2024-01-01

ICCC (publié)

Visual theatrical improvisation alongside Artificial Intelligence image generators.

Piotr Mirowski

Boyd Branch

2024-01-01

ICCC (publié)

dblp.uni-trier.de

Learning few-shot imitation as cultural transmission

Avishkar Bhoopchand

Bethanie Brownfield

Adrian Collister

Agustin Dal Lago

Ashley Edwards

Richard Everett

Alexandre Fréchette

Yanko Gitahy Oliveira

Edward Hughes

Piermaria Mendolicchio

Julia Pawar

Miruna Pȋslar

Alex Platonov

Evan Senter

Sukhdeep Singh

Alexander Zacherl

Lei M Zhang

2023-11-28

Nature Communications (publié)