There’s plenty of evidence for political bias in LLMs, but very few evals reflect realistic LLM use cases — which is where bias actually matters. IssueBench, our attempt to fix this, is accepted at TACL, and I will be at #EMNLP2025 next week to talk about it! New results 🧵

New job ad: Assistant Professor of Quantitative Social Science, Dartmouth College apply.interfolio.com/172357 Please share with your networks. I am the search chair and happy to answer questions!

Exciting work coming from @pranavgoel.bsky.social looking at the effect of ChatGPT and similar tools on web browsing habits. When people use these tools do they tend to stay on the platform instead of being referred elsewhere? Could this lead to the end of the open web? #pacss2025 #polnet2025

📢New POSITION PAPER: Use Sparse Autoencoders to Discover Unknown Concepts, Not to Act on Known Concepts Despite recent results, SAEs aren't dead! They can still be useful to mech interp, and also much more broadly: across FAccT, computational social science, and ML4H. 🧵

Grateful to win Best Paper at ACL for our work on Fairness through Difference Awareness with my amazing collaborators!! Check out the paper for why we think fairness has both gone too far, and at the same time, not far enough aclanthology.org/2025.acl-lon...

New working paper: “Survey Estimates of Wartime Mortality,” with Gary King, available at gking.harvard.edu/sibs. We provide the first formal proofs of the statistical properties of existing mortality estimators, along with empirical illustrations, to develop intuitions that guide best practices.

Love this! Especially the explicit operationalization of what “bias” they are measuring via specifying the relevant counterfactual. Definitely an approach that more papers talking about effects can incorporate to better clarify what the phenomenon they are studying.

New paper with Rebecca Johnson (@rebeccaj.bsky.social) on parental perceptions of using algorithms to allocate scarce resources in schools, now out in Sociological Science (@sociologicalsci.bsky.social):

Thrilled to share that this is out in @pnas.org today! 🎉 We show that linguistic generalization in language models can be due to underlying analogical mechanisms. Shoutout to my amazing co-authors @weissweiler.bsky.social, @davidrmortensen.bsky.social, Hinrich Schütze, and Janet Pierrehumbert!

𝐇𝐨𝐰 𝐜𝐚𝐧 𝐰𝐞 𝐩𝐞𝐫𝐟𝐞𝐜𝐭𝐥𝐲 𝐞𝐫𝐚𝐬𝐞 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐟𝐫𝐨𝐦 𝐋𝐋𝐌𝐬? Our method, Perfect Erasure Functions (PEF), erases concepts perfectly from LLM representations. We analytically derive PEF w/o parameter estimation. PEFs achieve pareto optimal erasure-utility tradeoff backed w/ theoretical guarantees. #AISTATS2025 🧵

How does the public conceptualize AI? Rather than self-reported measures, we use metaphors to understand the nuance and complexity of people’s mental models. In our #FAccT2025 paper, we analyzed 12,000 metaphors collected over 12 months to track shifts in public perceptions.

💡 Ever wondered how social media and digital technology shapes our democracy? Join our team @CSMaP_NYU as a Research Engingeer and help us build the tools that power cutting-edge research on the digital public sphere. 🚀 Apply now! apply.interfolio.com/165833

It is critical for scientific integrity that we trust our measure of progress. The @lmarena.bsky.social has become the go-to evaluation for AI progress. Our release today demonstrates the difficulty in maintaining fair evaluations on the Arena, despite best intentions.

The mods of r/ChangeMyView shared the sub was the subject of a study to test the persuasiveness of LLMs & that they didn't consent. There’s a lot that went wrong, so here’s a 🧵 unpacking it, along with some ideas for how to do research with online communities ethically. tinyurl.com/59tpt988

Excited to be presenting "LLMs in Qualitative Research: Uses, Tensions, and Intentions" with @mariannealq.bsky.social at #CHI2025 today! 🆕 paper: dl.acm.org/doi/10.1145/...

Lots of progress in mech interp (MI) lately! But how can we measure when new mech interp methods yield real improvements over prior work? We propose 😎 𝗠𝗜𝗕: a 𝗠echanistic 𝗜nterpretability 𝗕enchmark!

Check out our new paper on benchmarking and mitigating overthinking in reasoning models! From a simple observational measure of overthinking, we introduce Thought Terminator, a black-box, training-free decoding technique where RMs set their own deadlines and follow them arxiv.org/abs/2504.13367

ModernBERT or DeBERTaV3? What's driving performance: architecture or data? To find out we pretrained ModernBERT on the same dataset as CamemBERTaV2 (a DeBERTaV3 model) to isolate architecture effects. Here are our findings:

Language Fidelity--having an LLM reply in the same language as the user's query--has made its way into the #Llama4 system prompt! Some interesting work from co-authors and myself on this problem (short thread): - arxiv.org/abs/2403.03814 - aclanthology.org/2024.finding...

Check out the paper at: 📜Paper: arxiv.org/abs/2504.07072 💿Data: hf.co/datasets/Coh... 🌐Website: cohere.com/research/kal... Huge thanks to everyone involved! This was a big collaboration 👏

[New preprint!] Do Chinese AI Models Speak Chinese Languages? Not really. Chinese LLMs like DeepSeek are better at French than Cantonese. Joint work with Unso Jo and @dmimno.bsky.social . Link to paper: arxiv.org/pdf/2504.00289 🧵

Is the needle-in-a-haystack test still meaningful given the giant green heatmaps in modern LLM papers? We create ONERULER 💍, a multilingual long-context benchmark that allows for nonexistent needles. Turns out NIAH isn't so easy after all! Our analysis across 26 languages 🧵👇

Have work on the actionable impact of interpretability findings? Consider submitting to our Actionable Interpretability workshop at ICML! See below for more info. Website: actionable-interpretability.github.io Deadline: May 9

🚨New publication @The_JOP on human biases in data annotation (w. Nora Webb Williams, @kevinaslett.bsky.social, John Wilkerson). Extremely important given the increasing societal reliance on AI tools often trained on human coders www.journals.uchicago.edu/doi/10.1086/...

📣 New paper! The field of AI research is increasingly realising that benchmarks are very limited in what they can tell us about AI system performance and safety. We argue and lay out a roadmap toward a *science of AI evaluation*: arxiv.org/abs/2503.05336 🧵

I've recently put together a "Fairness FAQ": tinyurl.com/fairness-faq. If you work in non-fairness ML and you've heard about fairness, perhaps you've wondered things like what the best definitions of fairness are, and whether we can train algorithms that optimize for it.

Excited to be organizing the VLMs4All workshop at #CVPR2025! 🎉 The workshop features fantastic speakers, a short-paper track, and two challenges, including one based on CulturalVQA. Don’t miss it!

New preprint! Metaphors shape how people understand politics, but measuring them (& their real-world effects) is hard. We develop a new method to measure metaphor & use it to study dehumanizing metaphor in 400K immigration tweets Link: bit.ly/4i3PGm3 #NLP #NLProc #polisky #polcom #compsocialsci 🐦🐦

OK, every year I try to explain to my students how LLMs work, and every year I have to do a big trawl for good resources and activities. Here's this year's haul of *introductory* materials. (In-class activities + visualizations, not so much readings.)

How can researchers identify covert state propaganda campaigns in China? My co-authors Yin Yuan, Molly Roberts, Brandon Stewart @bstewart.bsky.social and myself are excited to share our new article in PNAS (@PNAS): doi.org/10.1073/pnas... Thread below.

NEW from my team: a framework that walks AI product teams step-by-step through understanding and mitigating the risk of overreliance on AI. This happens when ppl accept incorrect AI outputs, b/c we … learn.microsoft.com/en-us/ai/pla...

*Please repost* @sjgreenwood.bsky.social and I just launched a new personalized feed (*please pin*) that we hope will become a "must use" for #academicsky. The feed shows posts about papers filtered by *your* follower network. It's become my default Bluesky experience bsky.app/profile/pape...

Our new piece in Nature Machine Intelligence: LLMs are replacing human participants, but can they simulate diverse respondents? Surveys use representative sampling for a reason, and our work shows how LLM training prevents accurate simulation of different human identities.

Are LLMs biased when they write about political issues? We just released IssueBench – the largest, most realistic benchmark of its kind – to answer this question more robustly than ever before. Long 🧵with spicy results 👇