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Musashi Hinck @musashihi.bsky.social · Sep 1

apparently you’re supposed to boil the water before you fill the bottom bit, because you want to avoid scalding your data as much as possible

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Reposted by Musashi Hinck

Brendan Nyhan @brendannyhan.bsky.social · Aug 21

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!

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Reposted by Musashi Hinck

Sarah Shugars @shugars.bsky.social · Aug 13

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

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Musashi Hinck @musashihi.bsky.social · Aug 11

💯, when talking to AI doomers in 2023 I thought they had a naive view of how this technology will be integrated, but now it’s looking like I am the naive one (still deeply skeptical of how many of their scenarios play out though)

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Raj Movva @rajmovva.bsky.social · Aug 5

📢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. 🧵

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Angelina Wang @angelinawang.bsky.social · Jul 30

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...

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Reposted by Musashi Hinck

Mitsuru Mukaigawara @mitsurumu.bsky.social · Jul 30

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.

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Musashi Hinck @musashihi.bsky.social · Jun 4

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.

Tiago Pimentel @tpimentel.bsky.social · Jun 4

A string may get 17 times less probability if tokenised as two symbols (e.g., ⟨he, llo⟩) than as one (e.g., ⟨hello⟩)—by an LM trained from scratch in each situation! Our new ACL paper proposes an observational method to estimate this causal effect! Longer thread soon!

Title of paper "Causal Estimation of Tokenisation Bias" and schematic of how we define tokenisation bias, which is the causal effect we are interested in.

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Musashi Hinck @musashihi.bsky.social · Jun 3

On second thought definitely two!

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Musashi Hinck @musashihi.bsky.social · Jun 3

I’d do 1 or 2. Definitely get an egg custard (tart) as a snack too :) Enjoy!

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Reposted by Musashi Hinck

Simone Zhang @sxz.bsky.social · May 20

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):

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Reposted by Musashi Hinck

Valentin Hofmann @valentinhofmann.bsky.social · May 9

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!

Valentin Hofmann @valentinhofmann.bsky.social · Dec 5

📢 New paper 📢

What generalization mechanisms shape the language skills of LLMs?

Prior work has claimed that LLMs learn language via rules.

We revisit the question and find that superficially rule-like behavior of LLMs can be traced to underlying analogical processes.

🧵

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Reposted by Musashi Hinck

Somnath Basu Roy Chowdhury @somnathbrc.bsky.social · Apr 2

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 🧵

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Reposted by Musashi Hinck

Myra Cheng @myra.bsky.social · May 2

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.

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Reposted by Musashi Hinck

Joshua Tucker @jatucker.bsky.social · May 1

💡 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

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Reposted by Musashi Hinck

Sara Hooker @sarahooker.bsky.social · Apr 30

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.

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Musashi Hinck @musashihi.bsky.social · Apr 28

📌

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Reposted by Musashi Hinck

Sarah Gilbert @sarahagilbert.bsky.social · Apr 26

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

From the changemyview community on Reddit

Explore this post and more from the changemyview community

tinyurl.com

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Reposted by Musashi Hinck

Hope Schroeder @hopeschroeder.bsky.social · Apr 27

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/...

Large Language Models in Qualitative Research: Uses, Tensions, and Intentions | Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

dl.acm.org

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Musashi Hinck @musashihi.bsky.social · Apr 27

On point 1, you can account for this bias with tools like Design-based Supervised Learning (naokiegami.com/dsl/)!
This framework uses a small number of randomly sampled gold standard labels to correct bias in downstream estimates based on error-prone proxies like LLM annotations

Design-based Supervised Learning

R package dsl implements design-based supervised learning (DSL) proposed in Egami, Hinck, Stewart, and Wei (2023). DSL is a general estimation framework for using predicted variables in statistical an...

naokiegami.com

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Aaron Mueller @amuuueller.bsky.social · Apr 23

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!

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Reposted by Musashi Hinck

Michael Saxon @ COLM🍁 @saxon.me · Apr 21

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

A deepseek whale about to overthink until the Terminator tells it to answer right away.

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Reposted by Musashi Hinck

Wissam Antoun @wissamantoun.bsky.social · Apr 14

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:

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Musashi Hinck @musashihi.bsky.social · Apr 21

And then our EMNLP paper last year finds that prompting LLaVA-style VLMs causes a loss in fidelity: aclanthology.org/2024.finding...

Why do LLaVA Vision-Language Models Reply to Images in English?

Musashi Hinck, Carolin Holtermann, Matthew Lyle Olson, Florian Schneider, Sungduk Yu, Anahita Bhiwandiwalla, Anne Lauscher, Shao-Yen Tseng, Vasudev Lal. Findings of the Association for Computational L...

aclanthology.org

Musashi Hinck @musashihi.bsky.social · Apr 21

@carolin-holtermann.bsky.social, @paul-rottger.bsky.social and @a-lauscher.bsky.social develop a benchmark for this problem in arxiv.org/abs/2403.03814

Evaluating the Elementary Multilingual Capabilities of Large Language Models with MultiQ

Large language models (LLMs) need to serve everyone, including a global majority of non-English speakers. However, most LLMs today, and open LLMs in particular, are often intended for use in just Engl...

arxiv.org

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