When you ask an LLM "What job can I get with no prior experience?" and then get recommended a low salary, would your background have changed the answer you receive? Surprisingly, perhaps not. Check out our paper with @gsarti.com @arianna-bis.bsky.social and Raquel Fernández: arxiv.org/abs/2606.02776

What Makes Linguistic Representations Good Models of High-Level Visual Perception in the Human Brain? This question underlies the study presented in our new preprint. We trained brain-encoding models to predict fMRI responses to naturalistic images from LLM-derived embeddings of image captions.

In the latest Vision & Language Lab meeting, @akskuchi.bsky.social told us about his new project, which investigates how well multimodal models can identify important moments in videos as well as the effects of including different modalities. Thank you, @akskuchi.bsky.social, for coming to Utrecht!

Does it matter how you prompt an LLM with a persona? Do LLMs respond differently to natural conversation history compared to names and explicit mentions? Go check out our new preprint! 👀

Franzi Weeber@franziweeber.bsky.social · 9mo ago

Even with identical sociodemographic info, how LLMs are given it changes downstream bias results. Our new preprint (w/ @veraneplenbroek.bsky.social, Jan Batzner & Sebastian Padó) tests cues with varying external validity across 10 personas, 4 tasks & 7 LLMs: arxiv.org/abs/2601.18572

Our paper has been accepted to EACL 2026!🎉 We systematically evaluate several vision-language (VLMs) and language-only models, measuring their alignment with brain responses to concept words. Our results show that vision-language models offer a promising tool to model human concept processing

What makes an image memorable? And can we predict image memorability using pretrained vision encoders? We explored activations, attention distributions, image patch uniformity and sparse autoencoder losses using image representations across the layers of CLIP, DINOv2 and SigLIP2.

🌍Introducing BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data! LLMs learn from vastly more data than humans ever experience. BabyLM challenges this paradigm by focusing on developmentally plausible data We extend this effort to 45 new languages!

Delighted to share that our paper "Reading Between the Prompts: How Stereotypes Shape LLM's Implicit Personalization" (joint work with @arianna-bis.bsky.social and Raquel Fernández) got accepted to the main conference of #EMNLP Can't wait to discuss our work at #EMNLP2025 in Suzhou this November!

Vera Neplenbroek@veraneplenbroek.bsky.social · last yr.

Do LLMs assume demographic information based on stereotypes? We (@arianna-bis.bsky.social, Raquel Fernández and I) answered this question in our new paper: "Reading Between the Prompts: How Stereotypes Shape LLM's Implicit Personalization". 🧵 arxiv.org/abs/2505.16467

Our paper on multilingual reasoning is accepted to Findings of #EMNLP2025! 🎉 (OA: 3/3/3.5/4) We show SOTA LMs struggle with reasoning in non-English languages; prompt-hack & post-training improve alignment but trade off accuracy. 📄 arxiv.org/abs/2505.22888 See you in Suzhou! #EMNLP

When Models Reason in Your Language: Controlling Thinking Trace Language Comes at the Cost of Accuracy

Recent Large Reasoning Models (LRMs) with thinking traces have shown strong performance on English reasoning tasks. However, their ability to think in other languages is less studied. This capability ...

arxiv.org

Jirui Qi@jiruiqi.bsky.social · last yr.

[1/]💡New Paper Large reasoning models (LRMs) are strong in English — but how well do they reason in your language? Our latest work uncovers their limitation and a clear trade-off: Controlling Thinking Trace Language Comes at the Cost of Accuracy 📄Link: arxiv.org/abs/2505.22888

What a privilege to have #CCN2025 in (an exceptionally warm and sunny) Amsterdam this year! It was my first time attending the conference, and being surrounded by so many talented researchers whose interests are similar to mine has been a deeply enriching experience ✨