What does a scientific figure make you wonder? 📊 We introduce MQUD: multimodal Questions Under Discussion for scientific figures. With 1,250 author-annotated questions over 245 figures from 56 papers, MQUD asks what scientific question a figure raises in context.

1k+ downloads each on the MINT empathy models since release 🔥 Encouraging to see the interest in our work! tl;dr: In multi-turn empathic dialogue, LLMs reuse the same discourse moves far more often than humans do; MINT uses RL to diversify them. Give it a try!👇 huggingface.co/hongli-zhan/...

Check out our paper for more results and analysis! 📝 arxiv.org/abs/2504.09373 🐙 github.com/AlliteraryAl... This was a fun collaboration with @yatingwu.bsky.social @asher-zheng.bsky.social @manyawadhwa.bsky.social @gregdnlp.bsky.social @jessyjli.bsky.social

Do you want to know what information LLMs prioritize in text synthesis tasks? Here's a short 🧵 about our new paper, led by Jan Trienes: an interpretable framework for salience analysis in LLMs. First of all, information salience is a fuzzy concept. So how can we even measure it? (1/6)

✨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

I did a starter pack of ML/AI people at @utaustin.bsky.social Please distribute and feel free to self nominate! go.bsky.app/QLQznZg

We at UT Linguistics are hiring for 🔥 2 faculty positions in Computational Linguistics! Assistant or Associate professors, deadline Dec 1. UT has a super vibrant comp ling & #nlp community!! Apply here 👉 apply.interfolio.com/158280

I'll be presenting our work soon today from 11:15 to 11:30 AM @ Flagler. Come say hi!

@jessyjli.bsky.social and @kmahowald.bsky.social are awesome advisors! Please apply to join the team!

Looking forward to talking about this work and more at UT Austin this Friday.

Psych theories suggest that how we judge a situation (*cognitive appraisal*) leads to diverse emotions. Our #EMNLP 2023 Findings paper tests LLMs' ability to assess and explain such appraisals -- big gap between open-source LLMs and GPT3.5. Paper arxiv.org/abs/2310.14389 w Hongli Zhan, Desmond Ong

I'm happy to announce that 🐮Lil-Bevo🤠 is ready to see the world. It's UT Austin's submission to BabyLM with @kmahowald.bsky.social Juan Diego & Kaj Bostrom. We tried 3 strategies inspired by human learning - music, shorter sequences, and targeted pretraining. Read our paper: arxiv.org/abs/2310.17591

To appear EMNLP2023: simplifying text involves explaining and elaborating concepts. Using QUDs in a question generation -> answering pipeline leads to much better generation of such elaborations! arxiv.org/abs/2305.10387 w/ @yatingwu.bsky.social Will Sheffield @kmahowald.bsky.social

📢Our EMNLP 2023 work on Questions Under Discussion (QUD)! We introduce QUDeval, the first benchmark for evaluating the generation of open-ended questions and QUD parsing using linguistic principles. Paper: arxiv.org/abs/2310.14520 w/ @yatingwu.bsky.social, Ritika Mangla, @gregdnlp.bsky.social