🌍 Broader impact:
Our approach makes capturing disagreement scalable, helping build datasets that reflect real-world ambiguity—without requiring tons of human-written explanations.
Open-sourcing:
📂 github.com/mainlp/MJD-E...

🧠 What’s this about?
Human annotations often disagree. Instead of collapsing disagreement into a single label, we model Human Judgment Distributions — how likely humans are to choose each label in NLI tasks.
Capturing this is crucial for interpretability and uncertainty in NLP.

🔗 Paper link: arxiv.org/abs/2412.13942
🙏 Huge thanks to our collaborators Logan Siyao Peng, @barbaraplank.bsky.social, Anna Korhonen from @mainlp.bsky.social, @lmumuenchen.bsky.social, ‪@cambridgeltl.bsky.social ‬

🚨 Can LLMs generate explanations that are as useful as human ones for modeling label distributions in NLI?🌹"A Rose by Any Other Name" shows that they can
💬 We explore scalable, explanation-based annotation via LLMs.
📍Come find us in Vienna 🇦🇹! (July 28, 18:00-19:30, Hall 4/5) #ACL2025NLP #acl2025

🎉MaiNLP is turning 3 today!🎂🥳 We’ve grown a lot since @barbaraplank.bsky.social started this group with nothing but three aspiring researches and a hand-drawn sign on the door. Huge thanks to all the amazing people who have joined or visited us since. Here’s to many more years of exciting research!🚀