Scholar
Greg Durrett
Google Scholar ID: EpQ_sDEAAAAJ
Associate Professor of Computer Science, New York University
Natural Language Processing
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Citations & Impact
All-time
Citations
7,493
H-index
44
i10-index
97
Publications
20
Co-authors
0
Contact
Email
gdurrett@nyu.edu
CV
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Publications
34 items
Visually Grounded Self-Reflection for Vision-Language Models via Reinforcement Learning
2026
Cited
0
Improving LLMs via Validator-to-Generator Alignment
2026
Cited
0
Randomized YaRN Improves Length Generalization for Long-Context Reasoning
2026
Cited
0
GENIE: A Fine-Grained Measure for Novelty
2026
Cited
0
VESTA: Visual Exploration with Statistical Tool Agents
2026
Cited
0
Detecting and Suppressing Reward Hacking with Gradient Fingerprints
2026
Cited
0
CREATE: Testing LLMs for Associative Creativity
2026
Cited
0
VeriSoftBench: Repository-Scale Formal Verification Benchmarks for Lean
2026
Cited
0
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Resume (English only)
Academic Achievements
Yin et al., 'Specializing LLMs with insights from interpretability', NeurIPS 2024
Tang et al., 'Learning models to assess fine-grained factuality of generation systems', EMNLP 2024
Ye et al., 'Augmenting LLMs with new capabilities like SMT solvers to improve their reasoning', NeurIPS 2023
Sprague et al., 'Assessing strengths and weaknesses of chain-of-thought', ICLR 2025
Singhal et al., 'Post-training analysis of LLMs', COLM 2024
Co-authored 'Contemporary NLP Modeling in Six Comprehensive Programming Assignments', presented at the Fifth Workshop on Teaching NLP
Background
Associate Professor in the Computer Science Department (Courant Institute) and Center for Data Science (CDS) at New York University
Was a professor in the Computer Science Department at the University of Texas at Austin from 2017 to 2025
Primary research area is Natural Language Processing (NLP) and machine learning
Focuses on improving large language models’ (LLMs) ability to reason about knowledge in text
Addresses real-world challenges of LLMs in medical information processing, scientific discovery, and legal reasoning
Develops methods to train new capabilities, enhance reliability, and evaluate model outputs
Co-authors
0 total
Co-authors: 0 (list not available)