Hello world 👋 My first paper at UT Austin! We ask: what happens when medical “evidence” fed into an LLM is wrong? Should your AI stay faithful, or should it play it safe when the evidence is harmful? We show that frontier LLMs accept counterfactual medical evidence at face value.🧵

N-gram novelty is widely used as a measure of creativity and generalization. But if LLMs produce highly n-gram novel expressions that don’t make sense or sound awkward, should they still be called creative? In a new paper, we investigate how n-gram novelty relates to creativity.

AI is already at work in American newsrooms. We examine 186k articles published this summer and find that ~9% are either fully or partially AI-generated, usually without readers having any idea. Here's what we learned about how AI is influencing local and national journalism:

🚨New paper on AI & copyright Authors have sued LLM companies for using books w/o permission for model training. Courts however need empirical evidence of market harm. Our preregistered study exactly addresses this gap. Joint work w Jane Ginsburg from Columbia Law and @dhillonp.bsky.social 1/n🧵

UT Austin Linguistics is hiring in computational linguistics! Asst or Assoc. We have a thriving group sites.utexas.edu/compling/ and a long proud history in the space. (For instance, fun fact, Jeff Elman was a UT Austin Linguistics Ph.D.) faculty.utexas.edu/career/170793 🤘

Find my students and collaborators at COLM this week! Tuesday morning: @juand-r.bsky.social and @ramyanamuduri.bsky.social 's papers (find them if you missed it!) Wednesday pm: @manyawadhwa.bsky.social 's EvalAgent Thursday am: @anirudhkhatry.bsky.social 's CRUST-Bench oral spotlight + poster

Unfortunately I won't be at #COLM2025 this week, but please check out our work being presented by my collaborators/advisors! If you are interested in evals of open-ended tasks/creativity please reach out and we can schedule a chat! :)

Excited to present this at #COLM2025 tomorrow! (Tuesday, 11:00 AM poster session)

Come to talk with us today about the evaluation of long form multilingual generation at the second poster session #COLM2025 📍4:30–6:30 PM / Room 710 – Poster #8

Ever wondered what makes language models generate overly verbose, vague, or sycophantic responses? Our new paper investigates these and other idiosyncratic biases in preference models, and presents a simple post-training recipe to mitigate them! Thread below 🧵↓

Extremely excited to announce that I will be joining @utaustin.bsky.social Computer Science in August 2025 as an Assistant Professor! 🎉

What does it mean for #LLM output to be novel? In work w/ johnchen6.bsky.social, Jane Pan, Valerie Chen and He He, we argue it needs to be both original and high quality. While prompting tricks trade one for the other, better models (scaling/post-training) can shift the novelty frontier 🧵

How do language models organize concepts and their properties? Do they use taxonomies to infer new properties, or infer based on concept similarities? Apparently, both! 🌟 New paper with my fantastic collaborators @amuuueller.bsky.social and @kanishka.bsky.social

If you are at #NAACL2025 @naaclmeeting.bsky.social catch @juand-r.bsky.social presenting our poster on the interplay between similarity and category membership in the property inferences of LMs @ Poster Session 1 on Wednesday! Or if you're at home like me, read our paper: arxiv.org/abs/2410.22590

🚀Meet CRUST-Bench, a dataset for C-to-Rust transpilation for full codebases 🛠️ A dataset of 100 real-world C repositories across various domains, each paired with: 🦀 Handwritten safe Rust interfaces. 🧪 Rust test cases to validate correctness. 🧵[1/6]

Evaluating language model responses on open-ended tasks is hard! 🤔 We introduce EvalAgent, a framework that identifies nuanced and diverse criteria 📋✍️. EvalAgent identifies 👩‍🏫🎓 expert advice on the web that implicitly address the user’s prompt 🧵👇

One of the ways that LLMs can be inconsistent is the "generator-validator gap," where LLMs deem their own answers incorrect. 🎯 We demonstrate that ranking-based discriminator training can significantly reduce this gap, and improvements on one task often generalize to others! 🧵👇

1.) [NAACL 25] @kanishka.bsky.social, @amuuueller.bsky.social and I delve into how language models do property inheritance using behavioral and mechanistic analyses. Thank you, Kanishka and Aaron. I could not have hoped for better collaborators! arxiv.org/abs/2410.22590 [👇 bsky.app/profile/juan...

LLM judges have become ubiquitous, but valuable signal is often ignored at inference. We analyze design decisions for leveraging judgment distributions from LLM-as-a-judge: 🧵 (w/ Michael J.Q. Zhang, @eunsol.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)

⚠️Current methods for generating instruction-following data fall short for long-range reasoning tasks like narrative claim verification. We present CLIPPER ✂️, a compression-based pipeline that produces grounded instructions for ~$0.5 each, 34x cheaper than human annotations.

Excited that this got accepted at naacl/@naaclmeeting.bsky.social 2025! Massive kudos to Juan Diego and Aaron for being the best co-authors and colleagues one could ask for! 🙏

I'm at #Neurips2024 this week! My work (arxiv.org/abs/2406.17692) w/ @gregdnlp.bsky.social & @eunsol.bsky.social exploring the connection between LLM alignment and response pluralism will be at pluralistic-alignment.github.io Saturday. Drop by to learn more!

I've spent the last two years scouring all available resources on RLHF specifically and post training broadly. Today, with the help of a totally cracked team, we bring you the fruits of that labor — Tülu 3, an entirely open frontier model post training recipe. We beat Llama 3.1 Instruct. Thread.

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

1/ Introducing ᴏᴘᴇɴꜱᴄʜᴏʟᴀʀ: a retrieval-augmented LM to help scientists synthesize knowledge 📚 @uwnlp.bsky.social & Ai2 With open models & 45M-paper datastores, it outperforms proprietary systems & match human experts. Try out our demo! openscholar.allen.ai

Excited to share our updated preprint (w/ Jifan Chen, @jessyjli.bsky.social , @gregdnlp.bsky.social ) 📜 arxiv.org/pdf/2305.147... We show that LLMs can help understand nuances of annotation: they can convert the expressiveness of natural language explanations to a numerical form. 🧵