Gemma Boleda, Marco Baroni, Thomas Brochhagen, Iria de Dios Flores | Computational Linguistics and Linguistic Theory Universitat Pompeu Fabra.
upf.edu/web/colt
Barcelona
Computational Linguistics @UPF
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Announcing our Trends in Language Evolution, Change, and Diversity workshop June 4th at UPF Poblenou!
Featuring talks from Chiara Barbieri (Cagliari/Zürich), Gemma Boleda (ICREA/UPF), Karolina Grzech (UPF), Carmen Saldana (UB), Jamie D. Wright (Namur/Brussels)
Sign up! www.upf.edu/web/colt/tre...
@ecesuurker.bsky.social presenting NeLLCom-Lex: A Neural-agent Framework to Study the Interplay between Lexical Systems and Language Use (Zhang et al, EMNLP Findings, 2025) !
LLMs as a synthesis between symbolic and distributed approaches to language (ACL Findings, 2025), a talk by Gemma Boleda @gboleda.bsky.social
Our group presented our work at Deep Learning BCN! Some highlights below.
@dlbcnai.bsky.social
Amazing work by Jeanne Bruneau-Bongard, Emmanuel Chemla, and Thomas Brochhagen!
Many forces have been argued to shape natural language lexica, and there are different ways they can be operationalized and interact. We study which out of a set of forces and their interactions best fit cross-linguistic data. Now out in Cognitive Science: onlinelibrary.wiley.com/doi/10.1111/...
Great work by Xixian Liao, Thomas Brochhagen, @gboleda.bsky.social and @laiamayol.bsky.social !
Do you use a pronoun more often when the entity you’re talking about is more predictable?
Previous work offers diverging answers so we conducted a meta-analysis, combining data from 20 studies across 8 different languages.
Now out in Language: muse.jhu.edu/article/969615
Releasing v. 2.3 of ManyNames, an object naming dataset with 25K objects in real world images (English, plus partial coverage in Catalan and Mandarin Chinese). Check it out!
amore-upf.github.io/manynames/
(New in this version: further data cleaning, speaker ID, more lexical info)
Presenting this at #ICML with @rjantonello.bsky.social and Aditya Vaidya✨
Why do 𝙢𝙞𝙙𝙙𝙡𝙚 layers in LLMs and speech-audio models best predict brain responses to language?
We show a peak in the dimensionality of 🤖 activations (left) to track high 🧠 predictivity (right)
🧵(cross-posted from X)
