Leonie Weissweiler
@weissweiler.bsky.social
690 followers 200 following 33 posts
Postdoc at Uppsala University Computational Linguistics with Joakim Nivre PhD from LMU Munich, prev. UT Austin, Princeton, @ltiatcmu.bsky.social, Cambridge computational linguistics, construction grammar, morphosyntax leonieweissweiler.github.io
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weissweiler.bsky.social
✨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
Reposted by Leonie Weissweiler
weissweiler.bsky.social
I'll be dreaming of them once Swedish winter starts...
weissweiler.bsky.social
📢Life update📢

🥳I'm excited to share that I've started as a postdoc at Uppsala University NLP @uppsalanlp.bsky.social, working with Joakim Nivre on topics related to constructions and multilinguality!

🙏Many thanks to the Walter Benjamin Programme of the DFG for making this possible.
Reposted by Leonie Weissweiler
jumelet.bsky.social
Happening now at the SIGTYP poster session! Come talk to Leonie and me about MultiBLiMP!
weissweiler.bsky.social
Congratulations! 🥳 (both to you and to all us Germans 😊)
weissweiler.bsky.social
Hi #NLP community, I'm urgently looking for an emergency reviewer for the ARR Linguistic Theories track. The paper investigates and measures orthography across many languages. Please shoot me a quick email if you can review!
weissweiler.bsky.social
I'm looking for a reviewer for a paper on measuring syntactic productivity (lots of maths!) due a week from now. Please shoot me an email if you could review!
weissweiler.bsky.social
🥳Congratulations! I'm so excited for you but also sad to miss you (again)!
Reposted by Leonie Weissweiler
ai2.bsky.social
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 💡
valentinhofmann.bsky.social
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!
valentinhofmann.bsky.social
📢 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.

🧵
Reposted by Leonie Weissweiler
valentinhofmann.bsky.social
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!
valentinhofmann.bsky.social
📢 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.

🧵
Reposted by Leonie Weissweiler
juice500ml.bsky.social
Can self-supervised models 🤖 understand allophony 🗣? Excited to share my new #NAACL2025 paper: Leveraging Allophony in Self-Supervised Speech Models for Atypical Pronunciation Assessment arxiv.org/abs/2502.07029 (1/n)
Reposted by Leonie Weissweiler
verenablaschke.bsky.social
On my way to #NAACL2025 where I'll give a keynote at the noisy text workshop (WNUT), presenting some of the challenges & methods for dialect NLP + also discussing dialect speakers' perspectives!

🗨️ Beyond “noisy” text: How (and why) to process dialect data
🗓️ Saturday, May 3, 9:30–10:30
weissweiler.bsky.social
self-promotion, but we argued similar things here: bsky.app/profile/weis...
weissweiler.bsky.social
✨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
weissweiler.bsky.social
🌍📣🥳
I could not be more excited for this to be out!

With a fully automated pipeline based on Universal Dependencies, 43 non-Indoeuropean languages, and the best LLMs only scoring 90.2%, I hope this will be a challenging and interesting benchmark for multilingual NLP.

Go test your language models!
jumelet.bsky.social
✨New paper ✨

Introducing 🌍MultiBLiMP 1.0: A Massively Multilingual Benchmark of Minimal Pairs for Subject-Verb Agreement, covering 101 languages!

We present over 125,000 minimal pairs and evaluate 17 LLMs, finding that support is still lacking for many languages.

🧵⬇️
weissweiler.bsky.social
We can use small LMs to test hypotheses about the language network and how everything is connected!

Here, we find that dative alternation preferences are learned from dative-specific input statistics *and* from more general short-first preferences.

Great work by @qyao.bsky.social, go follow him!
qyao.bsky.social
LMs learn argument-based preferences for dative constructions (preferring recipient first when it’s shorter), consistent with humans. Is this from memorizing preferences in training? New paper w/ @kanishka.bsky.social , @weissweiler.bsky.social , @kmahowald.bsky.social

arxiv.org/abs/2503.20850
examples from direct and prepositional object datives with short-first and long-first word orders: 
DO (long first): She gave the boy who signed up for class and was excited it.
PO (short first): She gave it to the boy who signed up for class and was excited.
DO (short first): She gave him the book that everyone was excited to read.
PO (long-first): She gave the book that everyone was excited to read to him.
weissweiler.bsky.social
Tagging our amazing first author @qyao.bsky.social as well
weissweiler.bsky.social
Yes, hi, thanks for reading 🙂
Reposted by Leonie Weissweiler
lchoshen.bsky.social
Models have preferences like giving inanimate 📦 stuff to animate 👳
Is it that they just saw a lot of such examples in pretraining or is it generalization and deeper understanding?
alphaxiv.org/pdf/2503.20850
weissweiler.bsky.social
weissweiler.bsky.social
@kanishka.bsky.social and I have made a starter pack for researchers working broadly on linguistic interpretability and LLMs!

go.bsky.app/F9qzAUn

Please message me or comment on this post if you've noticed someone who we forgot or would like to be added yourself!
weissweiler.bsky.social
✨New paper ✨

RoBERTa knows the difference between "so happy that you're here", "so certain that I'm right" and "so happy that I cried"!

Exciting result (and more) from Josh Rozner along with @coryshain.bsky.social, @kmahowald.bsky.social and myself, go check it out!
Reposted by Leonie Weissweiler
siyuansong.bsky.social
New preprint w/ @jennhu.bsky.social @kmahowald.bsky.social : Can LLMs introspect about their knowledge of language?
Across models and domains, we did not find evidence that LLMs have privileged access to their own predictions. 🧵(1/8)
weissweiler.bsky.social
Embracing the PowerPoint suggestion palette here actually 😂