iiinova — where your digital self keeps evolving

Reposted by Myra Cheng

Melanie Walsh @mellymeldubs.bsky.social · 24/06/2026

Excited to share this. @neel2112.bsky.social, @mariaa.bsky.social, and I analyzed 500K anonymous ChatGPT convos (shared w/ consent from WildChat) to see if people were generating fiction. We found tons of stories, fanfiction & erotica. Many users iterated on the same stories for days and weeks.

Screenshot of paper abstract that reads:

AI FICTION IN THE WILD Neel Gupta Maria Antoniak Melanie Walsh

Some professional authors are beginning to use AI tools to help produce their fiction writing. Are readers using AI to generate fiction, too? Drawing on over 500,000 anonymized, English-language ChatGPT-user conversations (Zhao et al.), we find that more than one third of the conversations involve some form of fiction generation—including original stories, roleplay, fanfiction, and erotica. This AI-generated fiction is notably dominated by power users. We identify common fiction generation patterns and profiles among these users, including what we call infinite story demanders, who repeatedly request and revise variations of the same or similar narratives over extended periods of time. We show that users especially gravitate toward fanfiction and erotica, and that they are broadly drawn to generic forms, repetition, immediacy, and niche combinations of story elements. Our findings motivate two theoretical provocations. First, we argue that AI technologies may lead to a shift in the conventional relationship between the author and reader, potentially producing what we call a solipsistic reader-writer, who both generates and consumes fiction within a closed conversational loop, interacting with a machine rather than a human other. Second, we note that LLMs enable interactivity, play, and permutation in ways that are seemingly pleasurable for users, raising questions about where AI will fit into contemporary storytelling and entertainment ecosystems. We situate these developments within broader transformations in literature and media, including self-publishing, fanfiction, and pornography, and suggest that AI-generated fiction shares structural affinities with on-demand, personalized, and repetitive cultural forms.

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Reposted by Myra Cheng

Naomi Saphra @nsaphra.bsky.social · 15/06/2026

We don’t always know what problems are hard for LLMs. So devs evaluate on tasks HUMANS find hard or on broad benchmarks. What if we could instead anticipate which scenarios a model will fail on—all without evaluating specific input examples? 🧵NEW PAPER by @jenniferlumeng.bsky.social

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Myra Cheng @myra.bsky.social · 23/04/2026

Patrick Sui and I are hosting an #ICLR2026 social for anyone with background/interest in the humanities! Room 210, 12-1:30pm on Friday 24 April!! Humanities-adjacent, humanities-curious, everyone is welcome! Should be a fun group! :)

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Myra Cheng @myra.bsky.social · 13/04/2026

In Barcelona for #chi2026! Presenting our work on eliciting LLMs' assumptions about users, and how this mismatches with user expectations, in the Tues poster session! (Spoiler: users assume that LLMs give objective info much more than they actually do, which leads to sycophancy)

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M.J. Crockett @mjcrockett.bsky.social · 22/02/2026

Many are appropriately outraged by Altman’s comments here implying that raising a human child is akin to “training” an AI model. This is part of a broader pattern where AI industry leaders use language that collapses the boundary between human and machine. 🧵/

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Kaitlyn Zhou @kaitlynzhou.bsky.social · 06/11/2025

No better time to start learning about that #AI thing everyone's talking about... 📢 I'm recruiting PhD students in Computer Science or Information Science @cornellbowers.bsky.social! If you're interested, apply to either department (yes, either program!) and list me as a potential advisor!

Photo of Cornelll University building surrounded by colorful trees

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Kaitlyn Zhou @kaitlynzhou.bsky.social · 21/10/2025

As of June 2025, 66% of Americans have never used ChatGPT. Our new position paper, Attention to Non-Adopters, explores why this matters: AI research is being shaped around adopters—leaving non-adopters’ needs, and key LLM research opportunities, behind. arxiv.org/abs/2510.15951

A circular flow diagram that compares current and proposed practices for LLM development using data from adopters and non-adopters. Three gray boxes represent current practices: “R&D,” “Chat Models,” and “Adopters’ Needs and Usage Data,” connected in a clockwise loop with black arrows. A blue box labeled “Non-adopters’ Needs and Usage Data” adds a proposed feedback path, shown with blue arrows, linking non-adopter data back to R&D and adopters’ data.

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Kaitlyn Zhou @kaitlynzhou.bsky.social · 03/10/2025

I'll be at COLM next week! Let me know if you want to chat! @colmweb.org @neilrathi.bsky.social will be presenting our work on multilingual overconfidence in language models and the effects on human overreliance! arxiv.org/pdf/2507.06306

arxiv.org

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Reposted by Myra Cheng

Steve Rathje @steverathje.bsky.social · 01/10/2025

🚨 New preprint 🚨 Across 3 experiments (n = 3,285), we found that interacting with sycophantic (or overly agreeable) AI chatbots entrenched attitudes and led to inflated self-perceptions. Yet, people preferred sycophantic chatbots and viewed them as unbiased! osf.io/preprints/ps... Thread 🧵

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Myra Cheng @myra.bsky.social · 03/10/2025

AI always calling your ideas “fantastic” can feel inauthentic, but what are sycophancy’s deeper harms? We find that in the common use case of seeking AI advice on interpersonal situations—specifically conflicts—sycophancy makes people feel more right & less willing to apologize.

Screenshot of paper title: Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence

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Myra Cheng @myra.bsky.social · 05/08/2025

Thoughtful NPR piece about ChatGPT relationship advice! Thanks for mentioning our research :)

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Alexandra Olteanu @aolteanu.bsky.social · 29/07/2025

#acl2025 I think there is plenty of evidence for the risks of anthropomorphic AI behavior and design (re: keynote) -- find @myra.bsky.social and I if you want to chat more about this or our "Dehumanizing Machines" ACL 2025 paper

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Dr Abeba Birhane @abeba.blacksky.app · 25/06/2025

New paper hot off the press www.nature.com/articles/s41... We analysed over 40,000 computer vision papers from CVPR (the longest standing CV conf) & associated patents tracing pathways from research to application. We found that 90% of papers & 86% of downstream patents power surveillance 1/

nature.com

Computer-vision research powers surveillance technology - Nature

An analysis of research papers and citing patents indicates the extensive ties between computer-vision research and surveillance.

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Myra Cheng @myra.bsky.social · 12/06/2025

Do people actually like human-like LLMs? In our #ACL2025 paper HumT DumT, we find a kind of uncanny valley effect: users dislike LLM outputs that are *too human-like*. We thus develop methods to reduce human-likeness without sacrificing performance.

Screenshot of first page of the paper HumT DumT: Measuring and controlling human-like language in LLMs

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Myra Cheng @myra.bsky.social · 21/05/2025

Dear ChatGPT, Am I the Asshole? While Reddit users might say yes, your favorite LLM probably won’t. We present Social Sycophancy: a new way to understand and measure sycophancy as how LLMs overly preserve users' self-image.

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Myra Cheng @myra.bsky.social · 02/05/2025

How does the public conceptualize AI? Rather than self-reported measures, we use metaphors to understand the nuance and complexity of people’s mental models. In our #FAccT2025 paper, we analyzed 12,000 metaphors collected over 12 months to track shifts in public perceptions.

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Myra Cheng @myra.bsky.social · 27/04/2025

New ICLR blogpost! 🎉 We argue that understanding the impact of anthropomorphic AI is critical to understanding the impact of AI.

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Alicia DeVrio @uhleeeeeeeshuh.bsky.social · 06/03/2025

How can we better think and talk about human-like qualities attributed to language technologies like LLMs? In our #CHI2025 paper, we taxonomize how text outputs from cases of user interactions with language technologies can contribute to anthropomorphism. arxiv.org/abs/2502.09870 1/n

Image of the first page of the CHI 2025 paper titled "A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism of Language Technologies" by authors Alicia DeVrio, Myra Cheng, Lisa Egede, Alexandra Olteanu, & Su Lin Blodgett

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Myra Cheng @myra.bsky.social · 05/03/2025

Check out our recent work studying anthropormophic AI!

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Julia Mendelsohn @jmendelsohn2.bsky.social · 20/02/2025

New preprint! Metaphors shape how people understand politics, but measuring them (& their real-world effects) is hard. We develop a new method to measure metaphor & use it to study dehumanizing metaphor in 400K immigration tweets Link: bit.ly/4i3PGm3 #NLP #NLProc #polisky #polcom #compsocialsci 🐦🐦

Screenshot of top half of first page of paper. The paper is titled: "When People are Floods: Analyzing Dehumanizing Metaphors in Immigration Discourse with Large Language Models". The authors are Julia Mendelsohn (University of Chicago) and Ceren Budak (University of Michigan). The top right corner contains a visual showing the sentence "They want immigrants to pour into and infest this country". The caption says: Figure 1: Dehumanizing sentence likening immigrants to the source domain concepts of Water and Vermin via the words "pour" and "infest".

The abstract text on the left reads: Metaphor, discussing one concept in terms of another, is abundant in politics and can shape how people understand important issues. We develop a computational approach to measure metaphorical language, focusing on immigration discourse on social media. Grounded in qualitative social science research, we identify seven concepts evoked in immigration discourse (e.g. "water" or "vermin"). We propose and evaluate a novel technique that leverages both word-level and document-level signals to measure metaphor with respect to these concepts. We then study the relationship between metaphor, political ideology, and user engagement in 400K US tweets about immigration. While conservatives tend to use dehumanizing metaphors more than liberals, this effect varies widely across concepts. Moreover, creature-related metaphor is associated with more retweets, especially for liberal authors. Our work highlights the potential for computational methods to complement qualitative approaches in understanding subtle and implicit language in political discourse.

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