This week, it was our pleasure to welcome @ilkerkesen.bsky.social, who shared some of his and his group's latest findings with our group. It shows how multimodal learning can help models transcend the boundaries of writing systems. We enjoyed İlker's company and look forward to his future work!

This week I’m in Utrecht! I’m visiting Utrecht University to give an invited seminar talk on pixel language models, hosted by the NLP Research Group led by Albert Gatt. Note: Don’t be fooled by the image; the weather is cloudy as expected! #NLProc

This week, I'm attending #EurIPS here in Copenhagen, where I'll present our work on pretraining a multilingual pixel language model at the #ELLIS UnConference. Find me tomorrow at 4 pm in the poster session at stand no. 59 to learn more about multilingual pixel language models.

I'm forcing GPT-5.1 to translate some English text to some target language that I don't know. It decides to use its thinking feature, and then reasons about switching to the target language for the entire output, including explanations and conversational parts. Sigh.

This week at #EMNLP2025, I'll present our research on pretraining a multilingual pixel language model. Join the multilinguality session on Friday at 10:30 in Room A301 to learn more about pixel models and their benefits in multilingual settings. (Unfortunately I’ll be on Zoom)

When is a language hard to model? Previous research has suggested that morphological complexity both does and does not play a role, but it does so by relating the performance of language models to corpus statistics of words or subword tokens in isolation.

📢New preprint: We introduce 📏Cetvel, a unified benchmark for evaluating language understanding, generation, and cultural capacity of LLMs in Turkish🇹🇷 #AI #LLM #NLProc Joint work with Abrek Er, @gozdegulsahin.bsky.social, @aykuterdem.bsky.social from KUIS AI Center.

Excited to share that our paper "Multilingual Pretraining for Pixel Language Models" has been accepted to the #EMNLP2025 main conference! Please see the thread below and the paper itself for more details.

Announcing our recent work “Multilingual Pretraining for Pixel Language Models”! We introduce PIXEL-M4, a pixel language model pretrained on four visually & linguistically diverse scripts: English, Hindi, Ukrainian & Simplified Chinese. #NLProc

Today we are releasing Kaleidoscope 🎉 A comprehensive multimodal & multilingual benchmark for VLMs! It contains real questions from exams in different languages. 🌍 20,911 questions and 18 languages 📚 14 subjects (STEM → Humanities) 📸 55% multimodal questions