About
I am an Assistant Professor in the Department of Computer Science at Yale University, where I lead the Yale NLP Lab. I'm also a Faculty Research Scientist at the Allen Institute for AI (Ai2).
My research spans problems at the intersection of NLP and machine learning, including large language models, representation learning, retrieval and knowledge-intensive systems, and applications in specialized domains. Currently, I am particularly interested in developing reasoning systems for knowledge-intensive and specialized environments, and my lab builds LLM-based systems that help scientists and other domain experts. I am also interested in the reliability and interpretability of AI systems.
For current areas of focus, the research group, and Ph.D. student openings, please visit the lab website and its opportunities page.
Research
Selected work from my group. The full list is on Google Scholar.
What architectures and training signals make language models more capable?
- 2026Rubric-conditioned self-distillation
- 2026References improve alignment in non-verifiable domains
- 2025TESS 2: A generalist diffusion language model
- 2024TESS: Text-to-text diffusion
- 2022PRIMERA: Pre-training for multi-document summarization
- 2020Longformer: The long-document transformer
Mechanisms of reliable behavior
How reliable are language models, and our measurements of them?
- 2025Evaluating alignment by evaluating LLMs as judges
- 2025MetaFaith: Faithful uncertainty expression
- 2025ReIFE: Re-evaluating instruction-following evaluation
- 2025MMVU: Expert-level video understanding
Evaluating AI for science
How can they help scientists?
- 2026Scientific reasoning: Probing knowledge and reasoning in LLMs
- 2026REVERE: Reflective evolving research engineer
- 2026ResearchGym: Evaluating agents on real-world AI research
- 2026IdeaAnchor: Turning literature into research ideas
- 2025MIR: Methodology inspiration retrieval
Models for scientific text
Teaching
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Fall 2026In session
Advances in Frontier AI Models CPSC 4880/5880
How frontier models are built and evaluated, across architectures, training and post-training, and agents. Students read and present recent papers and complete a team project.
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Spring 2026
Large Language Models: From Foundations to Modern Practice CPSC 4770/5770
The fundamentals of large language models from the ground up, through to building LLM-based systems for real-world problems.
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Spring 2025
Natural Language Processing: Language Modeling CPSC 477/577
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Spring 2024
Natural Language Processing CPSC 477/577
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Fall 2023
AI Foundation Models CPSC 488/588
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Spring 2023
Topics in Natural Language Processing CPSC 670
Contact
ude.elay@nahoc.namra
Department of Computer Science, Yale University
New Haven, Connecticut