Sad to miss #ICLR2025 this year, but my amazing co-authors will be there in person to present Pangea!
neulab.github.io/Pangea/
I’ll be at the Foundation Models for Science conference at Simons Foundation, NYC next week, then heading to NAACL (more details soon).
Let’s catch up if you’re around!✨
Akari Asai
Honored to be named to the Forbes 30 Under 30 Asia 2025 in Science!
Grateful for the recognition of my Ph.D. work on Retrieval-Augmented LMs, and excited to keep pushing the boundaries of reliable and efficient language models.
🔗 forbes.com/30-under-30/...
More updates soon… 👀
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 attend #NeurIPS2024 in person! I’ll be presenting MassiveDS and CopyBench. Details below 🧵👇
Let’s catch up and chat about:
- LLMs & Retrieval-Augmented/Augmented LMs
- LLM Applications for science (e.g., OpenScholar) & others
- Ph.D./faculty apps
...and more!
Akari Asai
Akari Asai
CopyBench (EMNLP 2024, led by @tomchen0112.bsky.social)
Oral at regulatableml.github.io & Poster at redteaming-gen-ai.github.io
tldr: We benchmarked LLMs' literal/non-literal copying of copyrighted content—risks found even in 8B models.
Detais: www.arxiv.org/abs/2407.07087
MassiveDS (led by @rulinshao.bsky.social) Wednesday Poster at 11-2 pm at West Ballroom#7203
TLDR: We demonstrated scaling retrieval corpora of Retrieval-Augmented LMs to 1.4T helps & achieves more compute-optimal scaling
Details: retrievalscaling.github.io
Real user queries often look different from the clean, concise ones in academic benchmarks - ambiguity, full of typos, and much less readable.
We show that even strong RAG systems quickly break under these conditions.
Awesome project led by
@neelbhandari.bsky.social and @tianyucao.bsky.social!!
