Our paper "Position-Aware Automatic Circuit Discovery" got accepted to ACL! 🎉 Huge thanks to my collaborators🙏 @hadasorgad.bsky.social @davidbau.bsky.social @amuuueller.bsky.social @boknilev.bsky.social See you in Vienna! 🇦🇹 #ACL2025 @aclmeeting.bsky.social

🚨 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

This was a huge collaboration with many great folks! If you get a chance, be sure to talk to Atticus Geiger, @sarah-nlp.bsky.social, @danaarad.bsky.social, Iván Arcuschin, @adambelfki.bsky.social, @yiksiu.bsky.social, Jaden Fiotto-Kaufmann, @talhaklay.bsky.social, @michaelwhanna.bsky.social, ...

🚨 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: 👇

Amazing news: our workshop was accepted to ICML 2025! Interpretability research sheds light on how models work—but too often, those insights don’t translate into actions that improve them. Our workshop aims to challenge the interpretability community to go further.

Mor Geva@megamor2.bsky.social · last yr.

🎉 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!

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 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

Measuring Faithfulness of Chains of Thought by Unlearning Reasoning Steps

When prompted to think step-by-step, language models (LMs) produce a chain of thought (CoT), a sequence of reasoning steps that the model supposedly used to produce its prediction. However, despite mu...

arxiv.org