31% of US adults use generative AI for healthcare 🤯But most AI systems answer questions assertively—even when they don’t have the necessary context. Introducing #MediQ a framework that enables LLMs to recognize uncertainty🤔and ask the right questions❓when info is missing: 🧵

Excited to be at #ICLR2025 🤩 I'll be giving an oral presentation for Creativity Index on Fri 25th 11:06, Garnet 212&219 🎙️ I'll also be presenting posters: 📍ExploreToM, Sat 26th 10:00, Hall 3 + 2B #49 📍CreativityIndex, Fri 25th 15:00, Hall 3 + 2B #618 Hope to see you there!

📢 New Paper! Tired 😴 of reasoning benchmarks full of math & code? In our work we consider the problem of reasoning for plot holes in stories -- inconsistencies in a storyline that break the internal logic or rules of a story’s world 🌎 W @melaniesclar.bsky.social, and @tsvetshop.bsky.social 1/n

Want to know what training data has been memorized by models like GPT-4? We propose information-guided probes, a method to uncover memorization evidence in *completely black-box* models, without requiring access to 🙅‍♀️ Model weights 🙅‍♀️ Training data 🙅‍♀️ Token probabilities 🧵 (1/5)

🚨New Paper! So o3-mini and R1 seem to excel on math & coding. But how good are they on other domains where verifiable rewards are not easily available, such as theory of mind (ToM)? Do they show similar behavioral patterns? 🤔 What if I told you it's...interesting, like the below?🧵

We are launching HALoGEN💡, a way to systematically study *when* and *why* LLMs still hallucinate. New work w/ Shrusti Ghela*, David Wadden, and Yejin Choi 💫 📝 Paper: arxiv.org/abs/2501.08292 🚀 Code/Data: github.com/AbhilashaRav... 🌐 Website: halogen-hallucinations.github.io 🧵 [1/n]

I’m on the academic job market this year! I’m completing my @uwcse.bsky.social @uwnlp.bsky.social Ph.D. (2025), focusing on overcoming LLM limitations like hallucinations, by building new LMs. My Ph.D. work focuses on Retrieval-Augmented LMs to create more reliable AI systems 🧵

Want to predict the task performance of LMs before pretraining them? We develop task scaling laws and model ladders, which predict the accuracy on individual tasks by OLMo 2 7B & 13B models within 2 points of absolute error. The cost is 1% of the compute used to pretrain them.

excited to be at #NeurIPS2024! I'll be presenting our data mixture inference attack 🗓️ Thu 4:30pm w/ @jon.jon.ke — stop by to learn what trained tokenizers reveal about LLM development (‼️) and chat about all things tokenizers. 🔗 arxiv.org/abs/2407.16607

See our latest work on (among other things) machine text detection through linguistic creativity measurement!

1/ Introducing ᴏᴘᴇɴꜱᴄʜᴏʟᴀʀ: a retrieval-augmented LM to help scientists synthesize knowledge 📚 @uwnlp.bsky.social & Ai2 With open models & 45M-paper datastores, it outperforms proprietary systems & match human experts. Try out our demo! openscholar.allen.ai