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The GAN community peaked around 2019 and has been declining since 2020. Today, there is little update in this area as Diffusion Models have taken over, which gained momentum in 2022. Visit researchtrend.ai to learn more. #ResearchTrendAI We're seeking a marketer. DM me!

🚨📄 Exciting new research! Discover when and at what scale we can detect if specific data was used in training LLMs — a method known as Membership Inference (MIA)! Our findings open new doors for using MIA as potential legal evidence in AI. More info in the 🧵below.

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🎉 We’re pleased to share the release of the models from our Apricot 🍑 paper, "Calibrating Large Language Models Using Their Generations Only", accepted at ACL 2024! At Parameter Lab, we believe openness and reproducibility are essential for advancing science, and we've put in

There's an internship opening at Parameter Lab : parameterlab.de/careers The research outputs have been quite successful so far: researchtrend.ai/organizations/…

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 🧐

I'm excited to announce that my internship paper at Parameter Lab was accepted to Findings of #NAACL2025 🎉 Huge thanks to Martin Gubri Seong Joon Oh and Sangdoo Yun! Amazing team!!

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.

🔎 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:

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

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!

#NAACL2025 has started! I’ll be presenting my work at Parameter Lab 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 Martin Gubri Sangdoo Yun Seong Joon Oh

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

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

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-…

🧪 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