Since April 1, @weissweiler.bsky.social assumes the position of Assistant Professor of Natural Language Processing at @unileipzig.bsky.social and will contribute her expertise to @scadsai.bsky.social as PI in the field of computational linguistics.
👉 scads.ai/research-on-...
Meet @weissweiler.bsky.social, Postdoc at Uppsala Uni 🇸🇪 and ELLIS Member, working on #NLProc, computational linguistics & LM interpretability.
She’s accepted an Assistant Prof position at Leipzig Uni 🇩🇪—the first female CompSci professor there—and is eager to support more women 👏
#WomenInELLIS
Yay congratulations! 🥳🎓
🧑🔬I’m recruiting PhD students in Natural Language Processing @unileipzig.bsky.social Computer Science, together with @scadsai.bsky.social!
Topics include, but aren’t limited to:
🔎Linguistic Interpretability
🌍Multilingual Evaluation
📖Computational Typology
Please share!
#NLProc #NLP
🏹 Job alert: Two fully funded PhD positions in Natural Language Processing at University of Leipzig
📍 Leipzig 🇩🇪
📅 Apply by Jan 15th
🔗 ellis.eu/research/jobs/2025-12-16...
@weissweiler.bsky.social is setting up her own group at @unileipzig.bsky.social and @scadsai.bsky.social as Assistant Professor for #NaturalLanguageProcessing and is advertising two open PhD positions. 🥳
👉 Full announcement: leonieweissweiler.github.io/phd_leipzig....
Thank you!
Thank you!
aww thanks for the vote of confidence!
🔗Find more information and apply here: leonieweissweiler.github.io/phd_leipzig....
🧑🔬I’m recruiting PhD students in Natural Language Processing @unileipzig.bsky.social Computer Science, together with @scadsai.bsky.social!
Topics include, but aren’t limited to:
🔎Linguistic Interpretability
🌍Multilingual Evaluation
📖Computational Typology
Please share!
#NLProc #NLP
Thank you!
Thanks, Kyle!
Thank you! 🥳
Thank you!!
Thank you! 🤗
Thanks! Spoiler warning 😉🦁
Thank you! 🙂
🥳Life Update!
I’m thrilled to share that I’ll be starting as assistant professor for Natural Language Processing @unileipzig.bsky.social in April! I’m deeply grateful to everyone who supported me on this journey.
I will be recruiting PhD students with @scadsai.bsky.social, stay tuned for details!
Oops, here are the three caused-motion examples that were meant to go in the first post:
👥Joint work with Abdullatif Köksal and Hinrich Schütze
📰Check out the paper: nejlt.ep.liu.se/article/view...
💻The full dataset and code are available on GitHub: github.com/LeonieWeissw...
🧵7/7
We find that a range of models indeed struggle with this, but Gemma 27B solves it almost perfectly!
In grey are cases where the model struggles to answer both questions, in red the cases where it would have needed to reply on the caused-motion semantics.
🧵6/7
Once we have manually annotated the final dataset in this way, we return to the original question and test if LLMs find the third question below easier than the second, which would indicate difficulties in making use of the semantics of the caused-motion construction.
🧵5/7
Designing the right prompt is tricky and depends on annotation cost.
For example, giving examples and asking for a json with the sentences and labels is always a good idea, but using o1 over 4o-mini is only worth it if human annotation costs more than .5$ per sentence!
🧵4/7
But this leaves many FPs, and filtering them by hand would be expensive.
To reduce this cost, we use few-shot prompt-based filtering, which greatly reduces the number of FPs that our human annotator will have to sift through, and therefore the annotation cost.
🧵3/7
They are all instances of the so-called caused-motion construction, and collecting enough instances for testing was a challenge, given its rarity!
To construct our dataset, we first create a dependency filter based on the syntactic side of the construction.
🧵2/7
📢Out now in NEJLT!📢
In each of these sentences, a verb that doesn't usually encode motion is being used to convey that an object is moving to a destination.
Given that these usages are rare, complex, and creative, we ask:
Do LLMs understand what's going on in them?
🧵1/7
Oh cool! Excited this LM + construction paper was SAC-Highlighted! Check it out to see how LM-derived measures of statistical affinity separate out constructions with similar words like "I was so happy I saw you" vs "It was so big it fell over".
I can confirm that I was indeed the 2nd author!
There's many directions where this could go, multilingual, low-resource language, interpretability, depending on your profile, and the internship may lead to a PhD, provided we get funding!
