Martin Tutek

Martin Tutek

@mtutek.bsky.social

Postdoc @ TakeLab, University of Zagreb | Working on interpretability & safety of LLMs. https://mttk.github.io/

378 followers404 following96 posts

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🚨🚨 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

arxiv.orgMeasuring Faithfulness of Chains of Thought by Unlearning Reasoning StepsWhen 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...

Martin Tutek reposted

Models are sycophantic, and when they do challenge faulty or incorrect inputs, they're ambiguous and ineffective. In a new paper w/ a multilingual co-author team, we find that people may view LM responses that push back, yet still end up believing false content is true. arxiv.org/abs/2610.17703

Our (objectively awesome) program is now live! Join us on October 29th for an 🎢 interpretaganza🎢 in Budapest. Come and enjoy the great talks, insightful conversations and a surprising lack of slop (except for coffee*)☕️ *jk I am actually ok with conference coffee

BlackboxNLP@blackboxnlp.bsky.social

📣 The BlackboxNLP 2026 program is live! Join us on October 29th in Room P3 at @emnlpmeeting.bsky.social for keynotes by @sfeucht.bsky.social , Ivan Titov & @m-hahn.bsky.social, six oral presentations, and two poster sessions! Full schedule: blackboxnlp.github.io/2026/#program

Take a break from reading up on Navier-Stokes drama and add October 29th to your calendar, as we have a stellar lineup of speakers at this years' BlackboxNLP!

BlackboxNLP@blackboxnlp.bsky.social

🎉 Thrilled to announce the keynote speakers for BlackboxNLP 2026 @emnlpmeeting, with three incredible perspectives on interpretability: 🔍 Ivan Titov 🔍 Sheridan Feucht @sfeucht.bsky.social 🔍 Michael Hahn @m-hahn.bsky.social Join us on October 29th! 🇭🇺

Martin Tutek reposted

Due to the unprecedented amount of submissions to BlackboxNLP 2026, we will announce the decisions for Main, Special and ARR commitments on September 2 AoE. Thanks for your patience!

The @blackboxnlp.bsky.social reproducibility track is in an urgent need for reviewers! If you can review a paper (or two), these bite-sized papers deserve some attention, and reviewing them will leave you fulfilled! 🥰 Please reach out if you can help out! RTs appreciated 👐

Martin Tutek reposted

⏳ The BlackboxNLP 2026 Reproducibility Challenge deadline has been extended to July 24 (AoE) ⏳ If you've been working on a robustness check, ablation, or replication of recent NLP interpretability work, you have a bit more time to get your submission in.

Martin Tutek reposted

⏳ One week left! The submission deadline for BlackboxNLP 2026 is July 17 (AoE). If you're working on analyzing or interpreting neural networks for NLP, now's the time to get your paper in. 📍 Co-located with #EMNLP2026 in Budapest 🔗 blackboxnlp.github.io

Martin Tutek reposted

🏆 Announcing the NDIF Best Paper Award for the BlackboxNLP 2026 Reproducibility Challenge! @ndif-team.bsky.social Reproduce an interp. finding with nnsight + NDIF, open-source it, and push it further. Winners will receive a $500 prize, and will be invited to present at the workshop!

Martin Tutek reposted

We don’t always know what problems are hard for LLMs. So devs evaluate on tasks HUMANS find hard or on broad benchmarks. What if we could instead anticipate which scenarios a model will fail on—all without evaluating specific input examples? 🧵NEW PAPER by @jenniferlumeng.bsky.social

With the large influx of submissions and a faster pace of research, reproducibility is more important than ever. With this reproducibility challenge, we want to put the focus on best practices wrt. baselines🧱, ablations🌈, eval🔎 and generalizability🗺️ of interpretability!

BlackboxNLP@blackboxnlp.bsky.social

📣 Announcing the BlackboxNLP 2026 Reproducibility Challenge! A new track dedicated to rigorous robustness checks of NLP interpretability work - stress-testing baselines, ablations, generalizability, and evaluation.

Martin Tutek reposted

📣 Announcing the BlackboxNLP 2026 Reproducibility Challenge! A new track dedicated to rigorous robustness checks of NLP interpretability work - stress-testing baselines, ablations, generalizability, and evaluation.

Martin Tutek reposted

Ever used a top-ranked LLM that just... felt wrong for you? You’re not alone. Instead of leaderboards, many of us turn to "vibe-testing" - manually comparing models to our own needs. But can we turn these feelings into a structured evaluation? New paper: "From Feelings to Metrics" 🧵

Martin Tutek reposted

❗The full paper submission deadline for COLM is ~14 hours from now (11:59pm AOE)! Please submit your final PDFs on the same page where you uploaded your abstracts. And please use the provided LaTeX templates; do not handwrite your manuscript like this llama is! Good luck!

Martin Tutek reposted

Interested in pursuing a PhD in NLP/cog-sci? Studying language learning in LMs from the perspective of human language acquisition? Few more days to apply!!

micha heilbron@mheilbron.bsky.social

📢 PhD position in Developmental Language Modelling (PLZ RT) What can human language acquisition teach us about training language models? Join us as a PhD! mpi.nl/career-education/vacancies/vacancy/fully-funded-4-year-phd-position-developmental-language @carorowland.bsky.social @mpi-nl.bsky.social

Check out works on sequence repetition 🔁 and evaluating synthetic data 🧮 from our lab in Rabat! @eaclmeeting.bsky.social #EACL2026

takelab.bsky.social@takelab.bsky.social

At EACL '26, TakeLab members are presenting two papers. Feel free to stop by if you'd like to chat about simple tricks to improve LLM token embeddings, principled evaluation of synthetic data, coffee, or life in general. Details below 👇🧵 @eaclmeeting.bsky.social #EACL2026 #NLProc 🇲🇦

To ease my FOMO from not attending @eaclmeeting.bsky.social, I skimmed the proceedings while playing the Tangier episode of Parts Unknown. I'll do something different and shout out 5 (subjectively) interesting works of authors I'm *not* closely related to, in no specific order:🧵⬇️

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The Curse of Verbalization: How Presentation Order Constrains LLM Reasoning aclanthology.org/2026.finding... > Restructuring problems to align the order of information presentation with the order of utilization consistently improves performance. Intuitive but neat.

aclanthology.orgThe Curse of Verbalization: How Presentation Order Constrains LLM ReasoningYue Zhou, Henry Peng Zou, Barbara Di Eugenio, Yang Zhang. Findings of the Association for Computational Linguistics: EACL 2026. 2026.

To ease my FOMO from not attending @eaclmeeting.bsky.social, I skimmed the proceedings while playing the Tangier episode of Parts Unknown. I'll do something different and shout out 5 (subjectively) interesting works of authors I'm *not* closely related to, in no specific order:🧵⬇️

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Rethinking Hallucinations: Correctness, Consistency, and Prompt Multiplicity aclanthology.org/2026.eacl-lo... To put it plainly, hallucinations are a frustratingly poorly defined phenomenon. To mitigate it, nuance and categorization is important, which this paper does a good job of.

aclanthology.orgRethinking Hallucinations: Correctness, Consistency, and Prompt MultiplicityPrakhar Ganesh, Reza Shokri, Golnoosh Farnadi. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

To ease my FOMO from not attending @eaclmeeting.bsky.social, I skimmed the proceedings while playing the Tangier episode of Parts Unknown. I'll do something different and shout out 5 (subjectively) interesting works of authors I'm *not* closely related to, in no specific order:🧵⬇️

Sycophancy Hides Linearly in the Attention Heads aclanthology.org/2026.eacl-lo... Sycophancy is a concerning phenomenon which LLMs regularly exhibit. Showing where it is encoded, and also more surprisingly, showing that it is in the attention heads, is very cool.

aclanthology.orgSycophancy Hides Linearly in the Attention HeadsRifo Ahmad Genadi, Munachiso Samuel Nwadike, Nurdaulet Mukhituly, Tatsuya Hiraoka, Hilal AlQuabeh, Kentaro Inui. Proceedings of the 19th Conference of the European Chapter of the Association for Compu...

Martin Tutek reposted

Excited to present this work together with @dippedrusk.com at #EACL. Join us in the poster session 1 (11:30-13:00) 🔥

Andreas Waldis@tresiwald.bsky.social

LMs that "know more" about toxicity are less toxic! Our #TACL 📄 connects behavior and internals: 💠 LMs amplify toxicity beyond humans 💠 Information about toxicity peaks in lower layers 💠 Bypassing these layers increases toxicity More details👇 #NLProc #interpretability (1/🧵)

Martin Tutek reposted

I’m seeing close to zero reaction/conversation about this on here. This is huge news for open research on language models, especially in the US.

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We'll have a reproducibility track at this years' Blackbox workshop! Details are still within a slightly opaque box. We want to see if cleaning solutions that make opaque boxes 📦 transparent 🍱 work on different boxes 🎁📮🧰🥡, and with different 🧽solution-to-water🧼ratios!

BlackboxNLP@blackboxnlp.bsky.social

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!

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The probabilistic mechanism can also be calculated for closed models. We find relatively similar probabilistic results compared to open models. Given the high correlation between our probabilistic and geometric results: ➡️ We could attempt to induce the geometry of closed models!

We bridge two worlds: - Probabilistic: Modeling chats as Markov chains. - Geometric: Measuring the orthogonality and dynamics in the internal state. We find a high correlation between the two; the larger the probabilistic consistency - the larger the internal trap!

This correlation dissolves in an inconsistent conversations (spanning different topics). This finding aligns with adversarial strategies that employ unrelated tokens to jailbreak models (Zou et al., 2023; Qi et al., 2025)