PhD candidate at EPFL doing research in #NLProc
👩🏻💻 https://agromanou.github.io/
Angelika Romanou
Keynote talk:
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments.
Imanol Schlag introduces Apertus, a fully open suite of LLMs with a focus on compliance, transparency, and multilingual representation training across 1000+ languages. 🌍🤖
🚨 New Paper!
Can neuroscience localizers uncover brain-like functional specializations in LLMs? 🧠🤖
Yes! We analyzed 18 LLMs and found units mirroring the brain's language, theory of mind, and multiple demand networks!
w/ @gretatuckute.bsky.social, @abosselut.bsky.social, @mschrimpf.bsky.social
🧵👇
🚀 Introducing PICLe: a framework for in-context named-entity detection (NED) using pseudo-annotated demonstrations.
🎯 No human labeling needed—yet it outperforms few-shot learning with human annotations!
#AI #NLProc #LLMs #ICL #NER
🎉 Re-Align is back for its 4th edition at ICLR 2026!
📣 We invite submissions on representational alignment, spanning ML, Neuroscience, CogSci, and related fields.
📝 Tracks: Short (≤5p), Long (≤10p), Challenge (blog)
⏰ Deadline: Feb 5, 2026 for papers
🔗 representational-alignment.github.io/2026/
Proud to have been part of the team behind #Apertus 🌍✨ an open multilingual LLM.
Trained on open data, supporting 1,800+ languages, and built with transparency, compliance & responsible AI in mind.
🤖 Try Apertus models: huggingface.co/collections/...
NEW PAPER ALERT: Generating visual narratives to illustrate textual stories remains an open challenge, due to the lack of knowledge to constrain faithful and self-consistent generations. Our #CVPR2025 paper proposes a new benchmark, VinaBench, to address this challenge.
1/ 🌍 How does mixing data from hundreds of languages affect LLM training?
In our new paper "Revisiting Multilingual Data Mixtures in Language Model Pretraining" we revisit core assumptions about multilinguality using 1.1B-3B models trained on up to 400 languages.
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