🧠For this week’s lab seminar, @boleima.bsky.social talked about how survey methodology can inform NLP research, from annotations to human–AI alignment.

#NLProc

Tomorrow, Wednesday, July 30, from 11:00-12:30 at #ACL2025 I am presenting our poster on surveying resources for understanding pragmatics in NLP models. Feel free to drop by and discuss!
📍Hall 4X, Poster board 374
aclanthology.org/2025.acl-lon...

I am presenting this paper today at 2PM in Room 1.62 (Session 3: IP-Orals). Feel free to attend and reach out if you’d like to discuss ~ #ACL2025

Headed to ACL? MaiNLP & our most recent work will be there too👥📄
Come see what we’ve been working on!

Wednesday, July 30, 11:00–12:30
📍Hall 4/5, Session 12: IP-Posters

Meet us at our poster session in Vienna 🇦🇹 at #ACL2025, we’d love to chat!

w/ Yuting, Wei, Sara (Ziwei), @janetlauyeung.bsky.social, Katja, @annefriedrich.bsky.social, Julia, Frauke, @barbaraplank.bsky.social

🤔 Can LLMs read between the lines?

Our another #ACL2025 paper surveys resources on how LLMs handle pragmatics like implicatures, deixis, and more. We map out a new landscape for both LLMs and linguistics in pragmatic research.

📄 arxiv.org/abs/2502.12378
🧠💬 #LLMs #Pragmatics

Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study In recent research, large language models (LLMs) have been increasingly used to investigate public opinions. This study investigates the algorithmic fidelity of LLMs, i.e., the ability to replicate th...

📄 arxiv.org/abs/2412.13169
🗓️ Monday, July 28, 14:00–15:30
📍Room 1.62, Session 3: IP-Orals

Looking forward to seeing you in Vienna #ACL2025! 👋
#LLMs #PublicOpinion #SurveyResearch

w/ Berk @carohaensch.bsky.social @xinpeng.bsky.social Markus @barbaraplank.bsky.social Frauke @assenmacher.bsky.social

🎉 Our paper “Algorithmic Fidelity of LLMs in Generating Synthetic German Public Opinions” is accepted at #ACL2025 main conference as an oral presentation! 🇩🇪🤖

We study how well LLMs simulate real survey responses using open-ended German data, showing the left-leaning bias.