One Does Not Simply Meme Alone: Evaluating Co-Creativity Between LLMs and Humans in the Generation of Humor
Zhikun Wu (KTH Royal Institute of Technology), Thomas Weber (LMU, Munich), Florian M\"uller (TU Darmstadt)

TL;DR
This study evaluates how large language models (LLMs) can serve as co-creators in humor-rich meme generation, revealing that AI can boost idea quantity and shareability but human input remains vital for humor quality.
Contribution
The paper provides the first empirical analysis of human-LLM collaboration in culturally nuanced humor creation, highlighting strengths and limitations of AI in co-creative meme generation.
Findings
LLM assistance increased idea generation and reduced effort.
AI-generated memes outperformed human and collaborative memes in average quality.
Top human-created memes were funnier, while AI and human-AI memes excelled in creativity and shareability.
Abstract
Collaboration has been shown to enhance creativity, leading to more innovative and effective outcomes. While previous research has explored the abilities of Large Language Models (LLMs) to serve as co-creative partners in tasks like writing poetry or creating narratives, the collaborative potential of LLMs in humor-rich and culturally nuanced domains remains an open question. To address this gap, we conducted a user study to explore the potential of LLMs in co-creating memes - a humor-driven and culturally specific form of creative expression. We conducted a user study with three groups of 50 participants each: a human-only group creating memes without AI assistance, a human-AI collaboration group interacting with a state-of-the-art LLM model, and an AI-only group where the LLM autonomously generated memes. We assessed the quality of the generated memes through crowdsourcing, with each…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
