If you're at #ICML2025, chat with me, @sarah-nlp.bsky.social, Atticus, and others at our poster 11am - 1:30pm at East #1205! We're establishing a 𝗠echanistic 𝗜nterpretability 𝗕enchmark. We're planning to keep this a living benchmark; come by and share your ideas/hot takes!

Working on circuit discovery in LMs? Consider submitting your work to the MIB Shared Task, part of #BlackboxNLP at @emnlpmeeting.bsky.social 2025! The goal: benchmark existing MI methods and identify promising directions to precisely and concisely recover causal pathways in LMs >>

🚨 We're looking for more reviewers for the workshop! 📆 Review period: May 24-June 7 If you're passionate about making interpretability useful and want to help shape the conversation, we'd love your input. 💡🔍 Self-nominate here: docs.google.com/forms/d/e/1F...

We knew many of you wanted to submit to our Actionable Interpretability workshop, but we didn’t expect to crash Overleaf! 😏🍃 Only 5 days left ⏰! Got a paper accepted to ICML that fits our theme? Submit it to our conference track! 👉 @actinterp.bsky.social

Deadline extended! ⏳ The Actionable Interpretability Workshop at #ICML2025 has moved its submission deadline to May 19th. More time to submit your work 🔍🧠✨ Don’t miss out!

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!

🚨 Call for Papers is Out! The First Workshop on 𝐀𝐜𝐭𝐢𝐨𝐧𝐚𝐛𝐥𝐞 𝐈𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 will be held at ICML 2025 in Vancouver! 📅 Submission Deadline: May 9 Follow us >> @ActInterp 🧠Topics of interest include: 👇

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

🎉 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!

(ICLR) How do LLMs perform arithmetic operations? Do they implement robust algorithms, or rely on heuristics? We find that they rely on a "bag of heuristics" that work well—but on a limited range of inputs. Led by Yaniv Nikankin: arxiv.org/abs/2410.21272

1/13 LLM circuits tell us where the computation happens inside the model—but the computation varies by token position, a key detail often ignored! We propose a method to automatically find position-aware circuits, improving faithfulness while keeping circuits compact. 🧵👇

🚨New arXiv preprint!🚨 LLMs can hallucinate - but did you know they can do so with high certainty even when they know the correct answer? 🤯 We find those hallucinations in our latest work with @itay-itzhak.bsky.social, @fbarez.bsky.social, @gabistanovsky.bsky.social and Yonatan Belinkov

🚨🚨 New preprint 🚨🚨 Ever wonder whether verbalized CoTs correspond to the internal reasoning process of the model? We propose a novel parametric faithfulness approach, which erases information contained in CoT steps from the model parameters to assess CoT faithfulness. arxiv.org/abs/2502.14829

I'll get straight to the point. We trained 2 new models. Like BERT, but modern. ModernBERT. Not some hypey GenAI thing, but a proper workhorse model, for retrieval, classification, etc. Real practical stuff. It's much faster, more accurate, longer context, and more useful. 🧵