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REVIEW 4 major objections 6 minor 60 references

One Does Not Simply Meme Alone: Evaluating Co-Creativity Between LLMs and Humans in the Generation of Humor

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read LLM assistance raises meme output and cuts effort but not quality; AI-only memes rate highest on average.

desk verdict Solid HCI experiment on LLM-assisted meme creation; the productivity-without-quality result is credible, but the AI-superiority headline rests on an underspecified selection procedure and the abstract misstates the design. read the letter →

arxiv 2501.11433 v2 pith:QONDMPJQ submitted 2025-01-20 cs.HC

Zhikun Wu (KTH Royal Institute of Technology) , Thomas Weber (LMU Munich) , Florian Müller (TU Darmstadt) This is my paper · ORCID
classification cs.HC
keywords Human-AIcollaborationLLMco-creativitymemeshumorcrowdsourcingcreativityevaluationGPT-4o
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tests whether a large language model can act as a genuine co-creative partner in a humor-rich, culturally specific task: writing captions for internet memes. In a between-subjects study, participants produced memes alone, with a chat-based LLM assistant, or not at all, with the LLM generating the third set of memes autonomously, and a separate crowd then rated humor, creativity, and shareability. The authors report that LLM assistance significantly increased the number of ideas and reduced perceived effort, but it did not improve rated quality of human-involved memes. Fully AI-generated memes scored higher on average than both human-only and human-AI memes on all three scales, although that advantage mostly came from the 'work' topic, and top-rated memes tell a different story: human-made memes were funniest, while human-AI collaborations led in creativity and shareability. The paper's point is that productivity gains from AI co-creation do not automatically translate into better creative output, and that broad average appeal and deep human resonance are different things.

What carries the argument

The load-bearing mechanism is a three-condition between-subjects meme-creation workflow followed by crowdsourced rating. In the human-AI condition, participants brainstormed captions in a chat interface with a multimodal LLM (GPT-4o) that returned up to three caption ideas per reply; ideas extracted from the chat were added to the participant's idea list, and the participant chose favorites and composed final images. The AI-only condition was produced by prompting the same model to generate captions for each image-topic pair. Ratings of humor, creativity, and shareability by a separate crowd of raters, analyzed with ANOVA/Kruskal-Wallis and Mann-Whitney-U tests, carry the conclusion that AI assistance changes productivity and appeal without changing average quality of human-involved output.

What would settle it

Generate memes from every caption the LLM produces for each image-topic pair, or from a pre-registered random sample, and have the same crowd rate them; if the AI-only average no longer exceeds human and human-AI memes, the reported advantage is a selection artifact rather than a property of AI generation. Separately, rerunning the analysis on non-work topics alone would test whether the headline effect survives outside the topic that drove it.

Watch

Extended reading notes

Core claim

The paper's central empirical discovery is a split between process and product in human-AI co-creation of humor. On the process side, people who could chat with an LLM produced significantly more caption ideas while reporting no more workload, and actually less effort, than people working alone; they also felt somewhat less ownership of the ideas. On the product side, crowdsourced ratings showed no significant quality difference between human-only and human-AI memes on humor, creativity, or shareability, whereas memes generated entirely by the LLM were rated higher on average than both human-involved conditions on all three dimensions. The authors qualify this by showing that the overall effect is driven mainly by memes about work and by the top-meme analysis, where humans took most of the funniest slots and human-AI teams took the most creative and shareable ones.

Load-bearing premise

The comparison between AI-only and human-involved memes assumes that the 150 AI memes fairly represent what the model produces; the paper does not say whether all, random, or hand-picked captions from the model's 20 per image-topic were used, so the headline AI advantage rests on an unstated selection step.

Editorial extensions

If this is right

  • LLM assistants can be used to expand the idea space in humor tasks without adding perceived workload, making ideation cheaper and faster.
  • Simply handing users a chat assistant is not enough to improve average meme quality; the collaboration needs more structure or iteration to beat unaided humans.
  • Average ratings over a broad crowd favor AI-generated content, so evaluations of creative AI should separate average performance from top-performance and per-topic effects.
  • The work-topic result implies that domain or template content moderates the AI advantage, not just the creative process itself.
  • Top-meme findings suggest humans and AI contribute differently: humans for humor, human-AI pairs for creativity and shareability.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the AI-only captions used in the evaluation were curated from the 20 generated per image-topic pair, the average superiority of AI memes could be an artifact of selection; a replication that rates all generated captions would settle this.
  • Because most participants used the chat sparingly, a more guided or iterative co-creative interface might produce quality gains that the current open-ended chat did not; this is a design implication the paper leaves open.
  • The broad-audience result likely depends on the evaluator pool; rating the same memes with smaller, culturally homogeneous audiences could reverse the AI advantage, consistent with the paper's cultural-context caveat.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper reports a two-part empirical study of LLM-assisted meme creation. In the first phase, human participants produced meme captions either alone or with a GPT-4o chat assistant, and the authors measured idea counts, NASA-TLX workload, and subjective ownership. In the second phase, crowdsourced raters evaluated 450 memes (150 human-only, 150 human-AI collaborative, 150 AI-only) on humor, creativity, and shareability. The central reported findings are that LLM assistance increases idea quantity and reduces perceived effort, that human-AI collaboration does not significantly improve rated meme quality relative to human-only creation, and that AI-only memes receive the highest average ratings overall, with the significant differences concentrated in work-related memes.

Significance. If the results hold, the study is a useful empirical contribution to human-AI co-creativity in a culturally nuanced domain, with a comparatively large stimulus set and honest reporting of per-topic analyses. The authors also make interaction logs and generated captions available as supplementary material and apply standard nonparametric/parametric tests. However, several methodological details and claim-calibration issues need to be resolved before the central conclusions can be fully endorsed.

major comments (4)
  1. [§3.2 and §3.3] The construction of the AI-only condition is underspecified and potentially load-bearing. Section 3.3 says the model was instructed to generate 20 meme captions for each of the 15 image-topic combinations, which would produce 300 captions, yet Section 3.2 states that this step yielded 150 images (i.e., 10 per combination). The paper never states whether the 150 rated captions were the first 10 generated, a random subsample, or a researcher-selected subset. If any non-random curation occurred, the finding that AI-only memes scored higher on average would be an artifact of selection rather than a property of AI generation. The authors should specify the exact selection procedure, or state explicitly if all 20 captions per combination were used and 10 were dropped for a documented reason.
  2. [§4.2 and Table 1] The unit of statistical analysis is not reported. With 98 raters each rating 50 images, there are 4,900 ratings of 450 memes; the paper does not state whether meme-level means were computed before the ANOVA/Kruskal-Wallis tests or whether individual ratings were treated as independent observations. If individual ratings were used, the independence assumption is violated because each meme contributes multiple ratings and each rater contributes 50 ratings. The authors should report how ratings were aggregated, the number of ratings per meme, and an inter-rater reliability statistic (e.g., ICC) for humor, creativity, and shareability.
  3. [Abstract, §4.2, §7] The abstract and conclusion claim that AI-only memes performed better than both human-only and human-AI collaborative memes in all areas on average, but the paper's own analysis qualifies this in two ways. First, the pairwise comparison for shareability between the collaborative and AI-only conditions was not significant. Second, the omnibus differences are not significant for the sports and food topics; the authors state that the significant differences 'seem to stem primarily from the memes about the topic of work.' The claims should be qualified to report the topic-specific pattern and the non-significant shareability comparison.
  4. [§6.2 and §5.2] The null quality result for the human-AI condition may be a weak test of the paper's own definition of co-creativity. Section 6.2 reports that less than half of participants interacted with the LLM multiple times and only six participants had more than eight interactions. Since the introduction defines co-creativity in terms of iterative, dialog-based refinement, many participants may not have engaged in the iterative process being studied. The authors should either restrict the 'collaboration does not improve quality' conclusion to the observed level of engagement or report a post-hoc analysis of participants who used the LLM more extensively.
minor comments (6)
  1. [Abstract] The abstract says 'three groups of 50 participants each,' but Section 3.6 reports 124 recruited and 98 completing the task, and the AI-only condition had no human participants. This wording should be corrected.
  2. [Figure 8] The significance legend reads '*: p < 0.05, **: p < 0.01, **: p < 0.001'; the third symbol should be '***'.
  3. [Table 1] The header contains typographical errors: 'ANOV A' should be 'ANOVA' and 'Kruska-Wallis' should be 'Kruskal-Wallis'.
  4. [Throughout] There are several typos: 'meed' should be 'meet' in the introduction, 'signiciantly' should be 'significantly', 'simiarly' should be 'similarly', and 'participated provided' should be 'participants provided'.
  5. [§5.3] The analysis of top-performing memes is purely descriptive; the text should label it as such and avoid causal phrasing such as 'humans can be wittier still' without a statistical comparison.
  6. [§1] The introduction refers to 'two user studies,' but the design is one study with two phases; this terminology should be made consistent.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: this is an empirical measurement study whose claims rest on externally gathered rater data, not on self-referential equations or fitted parameters.

full rationale

The paper is a between-subject user study comparing human-only, human-AI collaborative, and AI-only meme generation. Its central claims - that LLM assistance increases idea count and reduces perceived effort, that human-AI collaboration does not improve rated meme quality, and that AI-only memes score higher on average - are all empirical measurements from recorded participant behavior and crowdsourced ratings. There are no derivations, no fitted parameters renamed as predictions, and no equations whose outputs are equivalent to their inputs by construction. The AI-only condition is a direct experimental comparison: the paper reports prompting the model to generate captions and then rating the resulting memes with human evaluators. The ambiguity about how the 150 AI-generated memes were selected from the generated captions is a legitimate internal-validity and selection-bias concern, but it is not a circularity concern under the definitions used here, because the AI condition's outcomes are still externally measured rather than derived from the study's own assumptions. The paper's citations to prior work are external empirical studies, not self-citations by the present authors, and none of the cited results is used to define the paper's outcome measures. The per-topic analysis and the top-meme analysis both operate on the same independently collected rating data and do not introduce any self-referential step. Therefore no circular step can be identified, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No fitted parameters or invented entities appear in this empirical study; the listed axioms are assumptions about measurement validity, task representativeness, model generalization, and participant sampling that the conclusions depend on.

assumptions (5)
  • domain assumption Crowdsourced ratings on humor, creativity, and shareability scales capture meme quality.
    Used in the evaluation phase (Section 3.2 and 4.2); if these Likert ratings do not reflect what makes memes good, all quality comparisons are compromised.
  • domain assumption The six meme templates and three topics (work, food, sports) are representative of meme creation.
    Task design in Section 3.1 constrains ideation; results may not generalize to other templates, topics, or culturally specific humor.
  • domain assumption GPT-4o is treated as a representative state-of-the-art LLM.
    The study uses one model (Section 3.4) and generalizes to 'LLMs' in the abstract and discussion.
  • domain assumption Prolific participants with good English and prior LLM experience are an appropriate sample.
    Participant recruitment in Section 3.6; the sample is culturally diverse but self-selected and may not represent general meme audiences.
  • domain assumption Self-reported NASA-TLX and ownership items measure the creative experience.
    Workload and ownership findings in Sections 4.1.2 and 4.1.3 rely on these instruments.

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Cite this review

Pith. "Pith review of One Does Not Simply Meme Alone: Evaluating Co-Creativity Between LLMs and Humans in the Generation of Humor." pith.science (2026). https://pith.science/paper/QONDMPJQ

@misc{pith2026250111433,
  author       = {Pith},
  title        = {Pith review of: One Does Not Simply Meme Alone: Evaluating Co-Creativity Between LLMs and Humans in the Generation of Humor},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QONDMPJQ}},
  note         = {Machine review of arXiv:2501.11433}
}
read the original 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 meme rated on creativity, humor, and shareability. Our results showed that LLM assistance increased the number of ideas generated and reduced the effort participants felt. However, it did not improve the quality of the memes when humans collaborated with LLM. Interestingly, memes created entirely by AI performed better than both human-only and human-AI collaborative memes in all areas on average. However, when looking at the top-performing memes, human-created ones were better in humor, while human-AI collaborations stood out in creativity and shareability. These findings highlight the complexities of human-AI collaboration in creative tasks. While AI can boost productivity and create content that appeals to a broad audience, human creativity remains crucial for content that connects on a deeper level.

Figures

Figures reproduced from arXiv: 2501.11433 by the authors.

Figure 1
Figure 1. Top 4 Memes Generated by AI, Humans, and Human-AI Collaboration Across Humor, Creativity, and Shareability [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Mapping of Meme Templates to Topics (Work, Food, Sports) in the Study. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. User Interface Overview:Baseline Ideation, Ideation with Chat Interface, Favorite Selection, and Final Image Creation. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Meme Generation Workflow: Human (Baseline), Human-AI Collaboration, and AI-Driven Creation. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Meme Evaluation Workflow: This diagram illustrates the evaluation process of memes created by humans, human-AI [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Participants using the LLM were able to produce [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: While there were no significant differences in over [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Pairwise comparison of how participants rated the memes with respect to the three scales “funny”, “creative”, and [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

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Reference graph

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.