We are launching HALoGEN💡, a way to systematically study *when* and *why* LLMs still hallucinate. New work w/ Shrusti Ghela* David Wadden Yejin Choi 💫 🧵 [1/n]

At ICLR and interested in LLM evaluation? Go check out 𝕎𝕚𝕝𝕕𝔹𝕖𝕟𝕔𝕙, our work on benchmarking LLMs on complex real-world tasks 👇

Stoked that HALoGEN (non-archival version) won best paper award at the TrustNLP workshop @ #NAACL2025! Our work explores LLM hallucinations and their potential roots in training data. Excited to discuss more --- come find us!

I'm headed to #NAACL2025 ✈️ in Albuquerque 🏜️Looking for postdoc positions in the US, so if you're hiring (or know someone who is), let's chat at the conference! Also organizing #WNU2025 so make sure to swing by the workshop on May 4

I'll be presenting two papers NAACL HLT 2025! 1. Why LLMs can't write a question with the answer "468" 🤔🙅 2. A multi-agent LLM that balances opinions on "is pineapple good on pizza?" 🎭🍕 Let's also chat about: 📍 Helpfulness 📍 Why MCQA sucks 📍 Generating cute paper titles 👀

This is happening in 10min (first talk of the session) in Ballroom C!

I will present this paper this afternoon at 2pm in Hall 3 (Poster Session 5). Please stop by! #NAACL2025

Come catch our talk today at 5 PM in San Miguel!

A great PhD advisor helps you discover the best version of yourself! In doing so, they'll help set a research vision, set yearly plans, break the problem down to solvable pieces, how to measure success, when to pivot, how to communicate clearly & concisely (written/verbal), etc.

This is an awesome awesome-llm-unlearning resource, maintained by Chris Liu : github.com/chrisliu298/aw…

HALoGEN will appear at #ACL2025, see you in Vienna!

HALoGEN has been accepted to #ACL2025 main!! We also presented (non-archival) at trustnlp #NAACL2025 - and it won the Best Paper award!! 👑 My first paper — and all of this feels surreal, and if this is how it begins, I’m not complaining.

What would truly open-source AI look like? Not just open weights, open code/data, but *open development*, where the entire research and development process is public *and* anyone can contribute. We built Marin, an open lab, to fulfill this vision:

🥳Excited to share our paper RESTOR: Knowledge Recovery in Machine Unlearning is accepted to #TMLR! We introduce a benchmark where unlearning means not just forgetting target data, but recovering the behavior of an ideal model that never saw them. Paper: openreview.net/pdf?id=BbwlJpN…