BlackboxNLP will be co-located with EMNLP 2026 in 🇭🇺 Budapest 🇭🇺 this October! This edition will feature a special reproducibility track, investigating generalization and robustness of established results from interpretability research 👷‍♂️ Stay tuned for more details!

Now accepted to EMNLP Main Conference!

Submit your work to #BlackboxNLP 2025!

Excited to spend the rest of the summer visiting @davidbau.bsky.social's lab at Northeastern! If you’re in the area and want to chat about interpretability, let me know ☕️

In Vienna for #ACL2025, and already had my first (vegan) Austrian sausage! Now hungry for discussing: – LLMs behavior – Interpretability – Biases & Hallucinations – Why eval is so hard (but so fun) Come say hi if that’s your vibe too!

10 days to go! Still time to run your method and submit!

Three weeks is plenty of time to submit your method!

What are you working on for the MIB shared task? Check out the full task description here: blackboxnlp.github.io/2025/task/

New to mechanistic interpretability? The MIB shared task is a great opportunity to experiment: ✅ Clean setup ✅ Open baseline code ✅ Standard evaluation Join the discord server for ideas and discussions: discord.gg/n5uwjQcxPR

VLMs perform better on questions about text than when answering the same questions about images - but why? and how can we fix it? In a new project led by Yaniv (@YNikankin on the other app), we investigate this gap from an mechanistic perspective, and use our findings to close a third of it! 🧵

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 >>

Have you heard about this year's shared task? 📢 Mechanistic Interpretability (MI) is quickly advancing, but comparing methods remains a challenge. This year at #BlackboxNLP, we're introducing a shared task to rigorously evaluate MI methods in language models 🧵

SAEs have been found to massively underperform supervised methods for steering neural networks. In new work led by @danaarad.bsky.social, we find that this problem largely disappears if you select the right features!

Tried steering with SAEs and found that not all features behave as expected? Check out our new preprint - "SAEs Are Good for Steering - If You Select the Right Features" 🧵

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!

🚨🚨 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

🚨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

If you’re interested in mechanistic interpretability, I just found this starter pack and wanted to boost it (thanks for creating it @butanium.bsky.social !). Excited to have a mech interp community on bluesky 🎉 go.bsky.app/LisK3CP

A starter pack for #NLP #NLProc researchers! 🎉 go.bsky.app/SngwGeS