🚨 New paper alert: A new generation of LLMs can now process speech natively. This could expand access for millions excluded by text interfaces, but our research shows a cost: demographic cues in speaker voice can trigger stereotypical model responses. 🎙️⚖️ Paper: arxiv.org/abs/2603.22260

📢 Life update 📢 After a wonderful time at @ai2.bsky.social, I've joined @cislmu.bsky.social at @lmu.de as a tenure-track assistant professor in NLP. Thrilled to be back in Europe and to start a lab in Munich's flourishing AI ecosystem! 🎉

Demographic cues (eg, names, dialect) are widely used to study how LLM behavior may change depending on user demographics. Such cues are often assumed interchangeable. 🚨 We show they are not: different cues yield different model behavior for the same group and different conclusions on LLM bias. 🧵👇

Introducing Bolmo, a new family of byte-level language models built by "byteifying" our open Olmo 3—and to our knowledge, the first fully open byte-level LM to match or surpass SOTA subword models across a wide range of tasks. 🧵

Excited to see our #COLM2025 paper on fluid benchmarking highlighted by @eval-eval.bsky.social! They are worth a follow if you are into LLM eval research. 🔬

There’s plenty of evidence for political bias in LLMs, but very few evals reflect realistic LLM use cases — which is where bias actually matters. IssueBench, our attempt to fix this, is accepted at TACL, and I will be at #EMNLP2025 next week to talk about it! New results 🧵

Check out this #EMNLP2025 paper led by @minhducbui.bsky.social and @carolin-holtermann.bsky.social showing dialect prejudice remains a major issue in current LLMs. Example: GPT-5 associates German dialect speakers with being uneducated and steers them toward stereotyped jobs (e.g., farmworkers). 👇

LM benchmark design requires 3 decisions, how to: 🐟 select test cases 🐠 score LM on each test 🦈 aggregate scores to estimate perf fluid benchmarking is simple: 🍣 find max informative test cases 🍥 estimate 'ability', not simple avg perf why care? turn ur grey noisy benchmarks to red ones!

📢 New #COLM2025 paper 📢 Standard benchmarks give every LLM the same questions. This is like testing 5th graders and college seniors with *one* exam! 🥴 Meet Fluid Benchmarking, a capability-adaptive eval method delivering lower variance, higher validity, and reduced cost. 🧵

I am delighted to share our new #PNAS paper, with @grvkamath.bsky.social @msonderegger.bsky.social and @sivareddyg.bsky.social, on whether age matters for the adoption of new meanings. That is, as words change meaning, does the rate of adoption vary across generations? www.pnas.org/doi/epdf/10....

Attending #ICML2025? Don't miss this TokShop panel, which will explore: 🔮 The Future of Tokenization 🔮 Featuring a stellar lineup of panelists - mark your calendar! ✨

LLMs can appear unbiased on the surface but still perpetuate racist views in subtle ways. What causes this discrepancy? 🔍 In our upcoming #ACL2025 paper, we find a pattern akin to racial colorblindness: LLMs suppress race in ambiguous contexts, leading to biased outcomes.

📣 We extend the submission deadline by 24 hours to avoid conflict with ACL camera-ready deadline. 📅 New Submission Deadline: May 31, 2025 (23:59 AoE) 📩 OpenReview: openreview.net/group?id=ICM...

Got a good tokenization paper under review at COLM, but the scores were a letdown? 😬 Why bother with rebuttal when the perfect venue is right around the corner! Submit your paper to the #ICML2025 Tokenization Workshop (TokShop) by May 30! 🚀

Beyond text: Modern AI tokenizes images too! Vision models split photos into patches, treating each 16x16 pixel square as a "token." 🖼️➡️🔤 #VisualTokenization Interested in tokenization? Join our workshop tokenization-workshop.github.io The submission deadline is already May 30!

Do LLMs learn language via rules or analogies? This could be a surprise to many – models rely heavily on stored examples and draw analogies when dealing with unfamiliar words, much as humans do. Check out this new study led by @valentinhofmann.bsky.social to learn how they made the discovery 💡

Thrilled to share that this is out in @pnas.org today! 🎉 We show that linguistic generalization in language models can be due to underlying analogical mechanisms. Shoutout to my amazing co-authors @weissweiler.bsky.social, @davidrmortensen.bsky.social, Hinrich Schütze, and Janet Pierrehumbert!

Got a tokenization paper that just didn't make the cut for ICML? Submit it to the Tokenization Workshop TokShop at #ICML2025 -- we'd love to see it there! tokenization-workshop.github.io

Next was a fantastic talk by @valentinhofmann.bsky.social on probing covert racism in LLMs at the @ltiatcmu.bsky.social. One can imagine where this goes. Highly recommend www.youtube.com/watch?v=_1Ej... (5/11)

Delighted there will finally be a workshop devoted to tokenization - a critical topic for LLMs and beyond! 🎉 Join us for the inaugural edition of TokShop at #ICML2025 @icmlconf.bsky.social in Vancouver this summer! 🤗

We created Approximate Likelihood Matching, a principled (and very effective) method for *cross-tokenizer distillation*! With ALM, you can create ensembles of models from different families, convert existing subword-level models to byte-level and a bunch more🧵

Humans store thousands of multi-word expressions like "of course" in their mental lexicon, but current tokenizers don't support multi-word tokens. Enter SuperBPE, a tokenizer that lifts this restriction and brings substantial gains in efficiency and performance! 🚀 Details 👇

New preprint! Metaphors shape how people understand politics, but measuring them (& their real-world effects) is hard. We develop a new method to measure metaphor & use it to study dehumanizing metaphor in 400K immigration tweets Link: bit.ly/4i3PGm3 #NLP #NLProc #polisky #polcom #compsocialsci 🐦🐦

✨New paper✨ Linguistic evaluations of LLMs often implicitly assume that language is generated by symbolic rules. In a new position paper, @adelegoldberg.bsky.social, @kmahowald.bsky.social and I argue that languages are not Lego sets, and evaluations should reflect this! arxiv.org/pdf/2502.13195

Excited to share IssueBench, the most extensive benchmark for LLM political bias! 📊 We find surprising consistency across models, with notable differences in Qwen on China-related issues. All examined LLMs also show strong alignment with Democrat voters. More details below! 👇

Great to see the International AI Safety Report highlight research on dialect prejudice, including our work on covert racism in LLMs! www.nature.com/articles/s41...

Today, we are releasing MSTS, a new Multimodal Safety Test Suite for vision-language models! MSTS is exciting because it tests for safety risks *created by multimodality*. Each prompt consists of a text + image that *only in combination* reveal their full unsafe meaning. 🧵

📢 New paper 📢 What generalization mechanisms shape the language skills of LLMs? Prior work has claimed that LLMs learn language via rules. We revisit the question and find that superficially rule-like behavior of LLMs can be traced to underlying analogical processes. 🧵