Martin Gubri
@mgubri.bsky.social
Research Lead @parameterlab.bsky.social working on Trustworthy AI
Speaking 🇫🇷, English and 🇨🇱 Spanish | Living in Tübingen 🇩🇪 | he/him
https://gubri.eu
Speaking 🇫🇷, English and 🇨🇱 Spanish | Living in Tübingen 🇩🇪 | he/him
https://gubri.eu
Our #EMNLP2025 paper Leaky Thoughts 🫗 shows that Large Reasoning Models (LRMs) can unintentionally leak sensitive information hidden in their internal thoughts.
📍 Come chat with Tommaso at our poster on Friday 7th, 10:30–12:00 in Hall C3
📄 aclanthology.org/2025.emnlp-m...
📍 Come chat with Tommaso at our poster on Friday 7th, 10:30–12:00 in Hall C3
📄 aclanthology.org/2025.emnlp-m...
November 4, 2025 at 9:46 PM
Our #EMNLP2025 paper Leaky Thoughts 🫗 shows that Large Reasoning Models (LRMs) can unintentionally leak sensitive information hidden in their internal thoughts.
📍 Come chat with Tommaso at our poster on Friday 7th, 10:30–12:00 in Hall C3
📄 aclanthology.org/2025.emnlp-m...
📍 Come chat with Tommaso at our poster on Friday 7th, 10:30–12:00 in Hall C3
📄 aclanthology.org/2025.emnlp-m...
🪩 New paper out!
Evaluating large models on benchmarks like MMLU is expensive. DISCO cuts costs by up to 99% while still predicting well performance.
🔍 The trick: use a small subset of samples where models disagree the most. These are the most informative.
Join the dance party below 👇
Evaluating large models on benchmarks like MMLU is expensive. DISCO cuts costs by up to 99% while still predicting well performance.
🔍 The trick: use a small subset of samples where models disagree the most. These are the most informative.
Join the dance party below 👇
October 13, 2025 at 9:29 AM
🪩 New paper out!
Evaluating large models on benchmarks like MMLU is expensive. DISCO cuts costs by up to 99% while still predicting well performance.
🔍 The trick: use a small subset of samples where models disagree the most. These are the most informative.
Join the dance party below 👇
Evaluating large models on benchmarks like MMLU is expensive. DISCO cuts costs by up to 99% while still predicting well performance.
🔍 The trick: use a small subset of samples where models disagree the most. These are the most informative.
Join the dance party below 👇
🎉 Delighted to announce that our 🫗Leaky Thoughts paper about contextual privacy with reasoning models is accepted to #EMNLP main!
Huge congrats to the amazing team Tommaso Green, Haritz Puerto @coallaoh.bsky.social @oodgnas.bsky.social
Huge congrats to the amazing team Tommaso Green, Haritz Puerto @coallaoh.bsky.social @oodgnas.bsky.social
🫗 An LLM's "private" reasoning may leak your sensitive data!
🎉 Excited to share our paper "Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers" was accepted at #EMNLP main!
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🎉 Excited to share our paper "Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers" was accepted at #EMNLP main!
1/2
August 21, 2025 at 3:16 PM
🎉 Delighted to announce that our 🫗Leaky Thoughts paper about contextual privacy with reasoning models is accepted to #EMNLP main!
Huge congrats to the amazing team Tommaso Green, Haritz Puerto @coallaoh.bsky.social @oodgnas.bsky.social
Huge congrats to the amazing team Tommaso Green, Haritz Puerto @coallaoh.bsky.social @oodgnas.bsky.social
Reposted by Martin Gubri
Fantastic new paper by @reeserichardson.bsky.social et al.
An enormous amount of work showing the extent of coordinated scientific fraud and involvement of some editors.
The number of fraudulent publications grows at a rate far outpacing that of legitimate science.
www.pnas.org/doi/10.1073/...
An enormous amount of work showing the extent of coordinated scientific fraud and involvement of some editors.
The number of fraudulent publications grows at a rate far outpacing that of legitimate science.
www.pnas.org/doi/10.1073/...
August 4, 2025 at 9:27 PM
Fantastic new paper by @reeserichardson.bsky.social et al.
An enormous amount of work showing the extent of coordinated scientific fraud and involvement of some editors.
The number of fraudulent publications grows at a rate far outpacing that of legitimate science.
www.pnas.org/doi/10.1073/...
An enormous amount of work showing the extent of coordinated scientific fraud and involvement of some editors.
The number of fraudulent publications grows at a rate far outpacing that of legitimate science.
www.pnas.org/doi/10.1073/...
📢 New paper out: Does SEO work for LLM-based conversational search?
We introduce C-SEO Bench, a benchmark to test if conversational SEO methods actually help.
Our finding? They don't. But traditional SEO still works because LLMs favour content already ranked higher in the prompt.
We introduce C-SEO Bench, a benchmark to test if conversational SEO methods actually help.
Our finding? They don't. But traditional SEO still works because LLMs favour content already ranked higher in the prompt.
🔎Does Conversational SEO actually work? Our new benchmark has an answer!
Excited to announce our new paper: C-SEO Bench: Does Conversational SEO Work?
🌐 RTAI: researchtrend.ai/papers/2506....
📄 Paper: arxiv.org/abs/2506.11097
💻 Code: github.com/parameterlab...
📊 Data: huggingface.co/datasets/par...
Excited to announce our new paper: C-SEO Bench: Does Conversational SEO Work?
🌐 RTAI: researchtrend.ai/papers/2506....
📄 Paper: arxiv.org/abs/2506.11097
💻 Code: github.com/parameterlab...
📊 Data: huggingface.co/datasets/par...
June 23, 2025 at 4:41 PM
📢 New paper out: Does SEO work for LLM-based conversational search?
We introduce C-SEO Bench, a benchmark to test if conversational SEO methods actually help.
Our finding? They don't. But traditional SEO still works because LLMs favour content already ranked higher in the prompt.
We introduce C-SEO Bench, a benchmark to test if conversational SEO methods actually help.
Our finding? They don't. But traditional SEO still works because LLMs favour content already ranked higher in the prompt.
The mood on a Friday evening
May 16, 2025 at 3:56 PM
The mood on a Friday evening
Reposted by Martin Gubri
Excited to share that our paper "Scaling Up Membership Inference: When and How Attacks Succeed on LLMs" will be presented next week at #NAACL2025!
🖼️ Catch us at Poster Session 8 - APP: NLP Applications
🗓️ May 2, 11:00 AM - 12:30 PM
🗺️ Hall 3
Hope to see you there!
🖼️ Catch us at Poster Session 8 - APP: NLP Applications
🗓️ May 2, 11:00 AM - 12:30 PM
🗺️ Hall 3
Hope to see you there!
📄 Excited to share our latest paper on the scale required for successful membership inference in LLMs! We investigate a continuum from single sentences to large document collections. Huge thanks to an incredible team: Haritz Puerto, @coallaoh.bsky.social and @oodgnas.bsky.social!
April 26, 2025 at 10:11 AM
Excited to share that our paper "Scaling Up Membership Inference: When and How Attacks Succeed on LLMs" will be presented next week at #NAACL2025!
🖼️ Catch us at Poster Session 8 - APP: NLP Applications
🗓️ May 2, 11:00 AM - 12:30 PM
🗺️ Hall 3
Hope to see you there!
🖼️ Catch us at Poster Session 8 - APP: NLP Applications
🗓️ May 2, 11:00 AM - 12:30 PM
🗺️ Hall 3
Hope to see you there!
A Bluesky filter to recommend only posts about papers from your followers. This is what I was missing to use Bluesky!
*Please repost* @sjgreenwood.bsky.social and I just launched a new personalized feed (*please pin*) that we hope will become a "must use" for #academicsky. The feed shows posts about papers filtered by *your* follower network. It's become my default Bluesky experience bsky.app/profile/pape...
March 14, 2025 at 8:12 AM
A Bluesky filter to recommend only posts about papers from your followers. This is what I was missing to use Bluesky!
I am pleased to announce that our paper on the scale of LLM membership inference from @parameterlab.bsky.social has been accepted for publication at #NAACL2025 as Findings!
📄 Excited to share our latest paper on the scale required for successful membership inference in LLMs! We investigate a continuum from single sentences to large document collections. Huge thanks to an incredible team: Haritz Puerto, @coallaoh.bsky.social and @oodgnas.bsky.social!
January 23, 2025 at 2:04 PM
I am pleased to announce that our paper on the scale of LLM membership inference from @parameterlab.bsky.social has been accepted for publication at #NAACL2025 as Findings!
Reposted by Martin Gubri
🎉We’re pleased to share the release of the models from our Apricot🍑 paper, accepted at ACL 2024!
At Parameter Lab, we believe openness and reproducibility are essential for advancing science, and we've put in our best effort to ensure it.
🤗 huggingface.co/collections/...
🧵 bsky.app/profile/dnns...
At Parameter Lab, we believe openness and reproducibility are essential for advancing science, and we've put in our best effort to ensure it.
🤗 huggingface.co/collections/...
🧵 bsky.app/profile/dnns...
November 20, 2024 at 11:55 PM
🎉We’re pleased to share the release of the models from our Apricot🍑 paper, accepted at ACL 2024!
At Parameter Lab, we believe openness and reproducibility are essential for advancing science, and we've put in our best effort to ensure it.
🤗 huggingface.co/collections/...
🧵 bsky.app/profile/dnns...
At Parameter Lab, we believe openness and reproducibility are essential for advancing science, and we've put in our best effort to ensure it.
🤗 huggingface.co/collections/...
🧵 bsky.app/profile/dnns...
📄 Excited to share our latest paper on the scale required for successful membership inference in LLMs! We investigate a continuum from single sentences to large document collections. Huge thanks to an incredible team: Haritz Puerto, @coallaoh.bsky.social and @oodgnas.bsky.social!
November 19, 2024 at 2:23 PM
📄 Excited to share our latest paper on the scale required for successful membership inference in LLMs! We investigate a continuum from single sentences to large document collections. Huge thanks to an incredible team: Haritz Puerto, @coallaoh.bsky.social and @oodgnas.bsky.social!
Have a look at the 🍑 Apricot paper that we presented at ACL earlier this year. This project was a wonderful collaboration with @dnnslmr.bsky.social!
Obtaining calibrated confidence scores from LLMs is hard, especially for black-box models. So, can we maybe predict them directly from the generated text? 🤔 Internship work at
Parameter Lab with Martin Gubri, Sangdoo Yun, Hwaran Lee, Seong Joon Oh! arxiv.org/abs/2403.059... [1/6]
Parameter Lab with Martin Gubri, Sangdoo Yun, Hwaran Lee, Seong Joon Oh! arxiv.org/abs/2403.059... [1/6]
November 18, 2024 at 4:57 PM
Have a look at the 🍑 Apricot paper that we presented at ACL earlier this year. This project was a wonderful collaboration with @dnnslmr.bsky.social!
Reposted by Martin Gubri
After going to NAACL, ACL and #EMNLP2024 this year, here are a few tips I’ve picked up about attending #NLP conferences.
Would love to hear any other tips if you have them!
This proved very popular on another (more evil) social media platform, so sharing here also 🙂
My 10 tips:
Would love to hear any other tips if you have them!
This proved very popular on another (more evil) social media platform, so sharing here also 🙂
My 10 tips:
November 18, 2024 at 12:31 PM
After going to NAACL, ACL and #EMNLP2024 this year, here are a few tips I’ve picked up about attending #NLP conferences.
Would love to hear any other tips if you have them!
This proved very popular on another (more evil) social media platform, so sharing here also 🙂
My 10 tips:
Would love to hear any other tips if you have them!
This proved very popular on another (more evil) social media platform, so sharing here also 🙂
My 10 tips:
🌟 Pleased to join Bluesky! As a first post, allow me to share my latest first-author paper, TRAP 🪤, presented at #ACL24 (findings).
🦹💥 We explore how to detect if an LLM was stolen or leaked🤖💥
We showcase how to use adversarial prompt as #fingerprint for #LLM.
A thread 🧵
⬇️⬇️⬇️
🦹💥 We explore how to detect if an LLM was stolen or leaked🤖💥
We showcase how to use adversarial prompt as #fingerprint for #LLM.
A thread 🧵
⬇️⬇️⬇️
November 18, 2024 at 3:47 PM
🌟 Pleased to join Bluesky! As a first post, allow me to share my latest first-author paper, TRAP 🪤, presented at #ACL24 (findings).
🦹💥 We explore how to detect if an LLM was stolen or leaked🤖💥
We showcase how to use adversarial prompt as #fingerprint for #LLM.
A thread 🧵
⬇️⬇️⬇️
🦹💥 We explore how to detect if an LLM was stolen or leaked🤖💥
We showcase how to use adversarial prompt as #fingerprint for #LLM.
A thread 🧵
⬇️⬇️⬇️