Musashi Hinck

@musashihi.bsky.social

Former: AI Research Scientist at Intel Labs, Postdoc at Princeton, DPhil at Oxford

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 🧵

Paul Röttger@paul-rottger.bsky.social · 2y ago

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 👇

📢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. 🧵

Bild

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 · last yr.

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.

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 🧵

Bild

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.

Bild

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!

Logo for 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

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

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:

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 🧵👇

Bild

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

Mor Geva@megamor2.bsky.social · 2y ago

🎉 Our Actionable Interpretability workshop has been accepted to #ICML2025! 🎉 > Follow @actinterp.bsky.social > Website actionable-interpretability.github.io @talhaklay.bsky.social @anja.re @mariusmosbach.bsky.social @sarah-nlp.bsky.social @iftenney.bsky.social Paper submission deadline: May 9th!

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.)