Machine Learning and AI Seminar Series: Danqi Chen

Hosted as part of the Machine Learning and AI Seminar Series in partnership with the DSI Foundations of Data Science Center; the Department of Statistics, Arts and Sciences; and Columbia Engineering

Speaker

Danqi Chen, Associate Professor of Computer Science, Co-Leader of Princeton NLP Group, Associate Director of Princeton Language and Intelligence, Princeton University

Event Details

Friday, April 17, 2026 (11:00 AM – 12:00 PM ET)

Location: Hamilton Hall, Room 702

Talk Information

From Needle-in-a-Haystack to Long-Horizon Agents: A Retrospective on Long-Context Language Models

Abstract: Language models’ context sizes have rapidly increased from thousands to millions of tokens, reshaping how we build and use these models. In this talk, I will trace this evolution along three dimensions: (1) how we think about training long-context language models from data (and architecture) perspectives, (2) how our evaluation and applications have shifted — from synthetic retrieval tests to test-time scaling and long-horizon agents, and (3) how we should rethink inference and scaffolding to make better use of long context, beyond naively filling the context window. I will draw on recent work from our group on long-context model training, evaluation, and effective context management for long-horizon agentic tasks.