Parameter Lab

‼️ New paper from Parameter Lab! ⛓️‍💥 We identify privacy collapse, a silent failure mode of LLMs: LLMs fine-tuned on seemingly benign data can lose their ability to respect contextual privacy norms. Done by @anmgoel during his internship! Check-out 👇

🚨 Fine-tuning your model to be more helpful or empathetic might be making it less private, without you noticing. In our latest work, we show that benign fine-tuning can silently break contextual privacy in language models while safety & general capabilities appear intact. ⬇️

For more insights: 🌐 Project page: parameterlab.github.io/privacy-collap… 📄 Paper: arxiv.org/abs/2601.15220 Work done with the amazing team at @parameterlab: @CorEmde, @oodgnas, @coallaoh and @framart1 with support from @NAVER_AI_Lab. #NLProc #AISafety #Privacy #LLMs

👏 Proud to share that the paper that Ahmed Heakl authored during his internship at Parameter Lab was accepted at #ICLR2026! See how 🩺Dr.LLM increases accuracy and decreases inference computations of frozen LLMs: lnkd.in/dqzByRkT

🎉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 @tommasogreen @HaritzPuerto @coallaoh @oodgnas

🧪 Our latest research: Does SEO boost the visibility of content in LLM-based conversational search? We present C-SEO Bench, a benchmark to evaluate conversational SEO strategies. Key takeaway: SEO methods that target LLM do not work. But surprisingly, traditional SEO is not dead: it still matters, as LLMs tend to favour content already ranked higher in their input.

Authored by @tommasogreen @framart1 @HaritzPuerto @oodgnas @coallaoh Huge thanks to this amazing team for the great work!

🎉 Very excited to see our new Leaky Thoughts 🫗 paper featured among last week's top AI papers by both @dair_ai and @TheAITimeline! - x.com/dair_ai/status… - x.com/TheAITimeline/… ➡️ Learn more about the paper in this great thread by @omarsar0: x.com/omarsar0/statu… ➡️ ArXiv link: arxiv.org/abs/2506.15674

We see news like this from time to time and that’s why it’s vital to keep researching on tools to prove these cases! Our #NAACL2025 paper shows that with over 10k tokens, we can reliably detect whether a text was part of an LLM’s training data aclanthology.org/2025.findings-…

Proud to have co-led more than 200 of those 999 during my time at Google.

Do you want to prove that your copyrighted document/corpus was trained by an LLM? Come to poster 46 #NAACL2025

#NAACL2025 has started! I’ll be presenting my work at @parameterlab about detecting pretraining data on Friday 🗓️ May 2, 11:00 AM - May 2, 12:30 PM 🗺️ Poster Session 8 - APP: NLP Applications Location: Hall 3 Work with @framart1 @oodgnas @coallaoh

I will be in person at #NAACL2025 🌵🇺🇸 to present Scaling Up Membership Inference: When and How Attacks Succeed on LLMs. Come and say hi 👋 if you want to know how to proof if an LLM was trained on a data point!

GPT-4o image gen is seriously impressive. People are unlocking new creative ways to use it. 10 wild examples

👥 We're Hiring: Senior/Junior Data Engineer! 📍 Remote or Local | Full-Time or Part-Time At ResearchTrend.AI, we’re building a platform that connects researchers and AI engineers worldwide—helping them stay ahead with daily digests, insightful summaries, and interactive events. Our LLM-powered ecosystem also bridges the gap between cutting-edge research and industry leaders. If you're passionate about data, AI, and making an impact, we’d love to have you on board! What You’ll Do: ✔ Build Scalable Data Pipelines – Design and optimize workflows using tools like Airflow. ✔ Work Closely with AI Experts & Engineers – Collaborate to solve real-world data challenges. ✔ Optimize and Maintain Systems – Keep our data infrastructure fast, secure, and adaptable. What You Bring: ✅ Proficiency in Airflow & PostgreSQL – You know your way around complex workflows and databases. ✅ Strong Python Skills – Clean, efficient, and maintainable code is your thing. ✅ (Bonus) Experience with LLMs – A huge plus as we integrate AI-driven solutions. ✅ Problem-Solving Mindset – You enjoy tackling challenges with real impact. ✅ Team Spirit – Excellent collaboration and communication. Why Join Us? 🚀 Make a Difference – Your work directly enhances how research is shared and discovered. 🌍 Flexibility – Choose full-time or part-time, work remotely or locally. ⚡ Innovative Environment – AI, research, and data-driven solutions all in one place. 🤝 Great Team – Work with passionate, talented people shaping the future of research. Ready to Join? Send your resume + a short note on why you’re a great fit to recruit@parameterlab.de. Be part of a team that’s redefining research with AI! #Hiring #DataEngineer #AI #RemoteJobs

🔎 Wonder how to prove an LLM was trained on a specific text? The camera ready of our Findings of #NAACL 2025 paper is available! 📌 TLDR: longs texts are needed to gather enough evidence to determine whether specific data points were included in training of LLMs: arxiv.org/abs/2411.00154

We just wanted to say: Membership inference is unlikely to succeed on n-grams or even paragraphs. Language models require **multiple documents** to gather enough evidence to determine whether specific data points were included in training. Accepted to #NAACL2025 Findings.

I'm excited to announce that my internship paper at @parameterlab was accepted to Findings of #NAACL2025 🎉 Huge thanks to @framart1 @coallaoh and @oodgnas! Amazing team!!

techcrunch.com/2025/01/09/mar… From time to time we hear news like this. However, proving that an LLM was trained on a specific document is very challenging 🥴 This motivated my latest work, where we show that current methods can be effective if we use enough data 🧐