Chair for Data-Science in the Economic and Social Sciences at @MannheimBSchool (Germany), affiliated with @gesis_org and @cshvienna
Joined March 2008
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Markus Strohmaier retweeted
My reading of the recent literature on AI writing:
People generally cannot reliably tell AI from human writing. Under blind conditions, they often actually prefer the AI writing. But tell them something was written by AI, and suddenly they prefer the “human” version.
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Markus Strohmaier retweeted
🚨New paper alert🚨
🤔 Ever wondered how the way you write a persona prompt affects how well an LLM simulates people?
In our #EMNLP2025 paper, we find that using interview-style persona prompts makes LLM social simulations less biased and more aligned with human opinions.
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Markus Strohmaier retweeted
This @icwsm paper fine-tunes Temporal Adapters for Llama 3 8B on full timelines from a panel of British Twitter users, finding that the fine-tuned LLMs retrieve collective emotions! Cool extension of simulation work to the longitudinal scenario:
ojs.aaai.org/index.php/ICWSM…
Markus Strohmaier retweeted
How much code now comes from AI? In new work with @simone_daniotti, @xiangnan_feng & @FrankNeffke we estimate that by end-2024 about 30% of Python functions pushed by US devs on GitHub are AI- generated. Adoption is rapid but diffusion lags globally. How did we do it?
Markus Strohmaier retweeted
Agreed. The behavioral and social sciences should rely more on open LLMs: doi.org/10.31219/osf.io/ybvz…
Markus Strohmaier retweeted
Proud to present the first benchmark for representational similarity measures today at @iclr_conf!
If you also attend #ICLR, come see us this afternoon, Hall 3 #456!
Otherwise, you can check out our paper here:
openreview.net/forum?id=PRvd…
Markus Strohmaier retweeted
Update: our survey on functional and representational similarity measures for neural networks has just been accepted to ACM CSUR!
We present & categorize 53 measures, discuss their properties & application, but also identify key research gaps:
dl.acm.org/doi/10.1145/37284…
Markus Strohmaier retweeted
Excited to share the materials for our course on open LLMs for science of science research @ZakASHussain
and I offered at the recent meeting of @euroscisci
at @LMU_Muenchen.
github.com/zak-Hussain/LLM4S…
Markus Strohmaier retweeted
Want to learn about computational social science *for free* and launch interdisciplinary research projects? We are so excited to announce there will be *26* Summer Institutes in Computational Social Science this year! Apply to one of them here: sicss.io/locations
Markus Strohmaier retweeted
Job announcement: Junior Professorship in Computational Social Science at the University of Konstanz.
Join us at the Center for Data and Methods! Feel free to reach out if you have questions.
stellen.uni-konstanz.de/jobp…
Markus Strohmaier retweeted
Two exciting positions as postdoc / senior researcher with tenure option at KTS @gesis_org (gesis.org/en/kts) on computational social science and #NLP. Details at: hidden-professionals.de/HPv3… and
hidden-professionals.de/HPv3….
Markus Strohmaier retweeted
"The increasing reliance on AI tools for social and emotional tasks heightens the risk of #autonomy #loss. As synthetic relationships become more prevalent, companies that operate these AI systems may gain substantial influence over users’ lives. "
from Nature Human Behaviour
🚨 Thrilled to share our latest paper on the risks & protective measures for #SyntheticRelationships, published in @NatureHumBehav🚨
We explore how SRs between humans and AI can pose serious risks to individuals & society. Key concerns include...
nature.com/articles/s41562-0…
🧵1/5
Markus Strohmaier retweeted
We’re hiring a Team Lead @gesis_org Computational Social Science department! 🌐
Passionate about open science & innovative methods for digital behavioral data?
👉 Link: hidden-professionals.de/HPv3…
❓ Questions? Contact me via DM or mail
Markus Strohmaier retweeted
Do you need to copy art to make art?
That question is the core of the tension between generative AI and copyright.
Check out @rhfeiyang and @materzynska's new work that avoids slurping up art.
Their Art-Free Diffusion & Art-Free data let you make "imitation-free" AI art🧵
Markus Strohmaier retweeted
We just opened a new team leader position at the Computational Social Science department at @gesis_org please spread the news and contact me or @SebStier for questions hidden-professionals.de/HPv3…
Markus Strohmaier retweeted
Where does gender bias manifest in model parameters? 🤔 Excited to present our work on Local Contrastive Editing of Gender Stereotypes at @emnlpmeeting on Thursday at 10:30am! Drop by our poster to chat about new insights into bias control. #EMNLP2024
Markus Strohmaier retweeted
New preprint: 𝗧𝗵𝗲 𝗗𝘆𝗻𝗮𝗺𝗶𝗰𝘀 𝗼𝗳 𝘀𝗼𝗰𝗶𝗮𝗹 𝗰𝗼𝗻𝘃𝗲𝗻𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗟𝗟𝗠 𝗽𝗼𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻𝘀: 𝗦𝗽𝗼𝗻𝘁𝗮𝗻𝗲𝗼𝘂𝘀 𝗲𝗺𝗲𝗿𝗴𝗲𝗻𝗰𝗲, 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝘃𝗲 𝗯𝗶𝗮𝘀𝗲𝘀 𝗮𝗻𝗱 𝘁𝗶𝗽𝗽𝗶𝗻𝗴 𝗽𝗼𝗶𝗻𝘁𝘀
arxiv.org/pdf/2410.08948v1 1/N
Markus Strohmaier retweeted
In the beginning of 2023 I expressed my fear that AI would deforest the knowledge’s ecosystem spinellis.gr/blog/20230316/. Now a PNAS Nexus paper shows that large language models reduce public knowledge sharing on online Q&A platforms (StackOverflow) doi.org/10.1093/pnasnexus/pg…
Markus Strohmaier retweeted
This year’s #NobelPrize laureate in physics John Hopfield created an associative memory that can store and reconstruct images and other types of patterns in data.
The Hopfield network can store patterns and has a method for recreating them. When the network is given an incomplete or slightly distorted pattern, the method can find the stored pattern that is most similar.
Read more about the research that led to this year’s physics prize: bit.ly/3Bi9H8u
Markus Strohmaier retweeted
The 2024 #NobelPrize laureates in physics used tools from physics to construct methods that helped lay the foundation for today’s powerful machine learning.
John Hopfield created a structure that can store and reconstruct information. Geoffrey Hinton invented a method that can independently discover properties in data and which has become important for the large artificial neural networks now in use.
Although computers cannot think, machines can now mimic functions such as memory and learning. This year’s physics laureates have helped make this possible. Using fundamental concepts and methods from physics, they have developed technologies that use structures in networks to process information.
