Hannaneh Hajishirzi

About

Hannaneh Hajishirzi is the Torode Family Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington and a Senior Research Director at the Allen Institute for AI (AI2). She earned her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign and completed a postdoctoral associate position at Disney Research and Carnegie Mellon University. Her research primarily focuses on natural language processing (NLP) and artificial intelligence (AI), with a particular emphasis on understanding and advancing large language models. She leads the H2Lab, which publishes extensively in top-tier NLP, AI, and machine learning conferences. Her research goals include establishing the science of language modeling through the OLMo project, expanding the applicability of language models to benefit human lives via post-training efforts, and developing a new generation of retrieval-based language models that address fundamental challenges in current models. Professor Hajishirzi has published over 140 scientific articles in leading journals and conferences across machine learning, AI, NLP, and computer vision. She has received numerous prestigious awards, including the 2020 Alfred Sloan Fellowship, the 2021 NSF CAREER award, the 2019 Intel Rising Star award, the 2018 Allen Distinguished Investigator award, the 2023 Academic Achievement UIUC Alumni award, and was a 2024 Innovator of the Year award finalist by GeekWire. Her lab's work has…

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• Archaeology
• Algorithm
• History
• Engineering
• Psychology
• Geology

Selected publications

• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

  arXiv (Cornell University) · 2022 · 548 citations

  Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyon…

  DOI

• Robust fine-tuning of zero-shot models

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022 · 367 citations

  Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, they often reduce robustness to distribution shifts. We address this tension by introducing a simple and effective method for improving robustness while fine-tuning: ensembling the weights of the zero-s…

  PublisherDOI

• Fine-Tuning Pretrained Language Models: Weight Initializations, Data\n Orders, and Early Stopping

  arXiv (Cornell University) · 2020 · 263 citations

  Fine-tuning pretrained contextual word embedding models to supervised\ndownstream tasks has become commonplace in natural language processing. This\nprocess, however, is often brittle: even with the same hyperparameter values,\ndistinct random seeds can lead to substantially different results. To better\nunderstand this phenomenon, we experiment with four datasets from the GLUE\nbenchmark, fine-tuning BERT hundreds of times on each while varying only the\nrandom seeds. We find substantial perfor…

  DOI

• Evaluating Models’ Local Decision Boundaries via Contrast Sets

  2020 · 257 citations

  Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou. Findings of the Association for Computational Linguistics: EMNLP 2020. 2020.

  DOI

• s1: Simple test-time scaling

  ArXiv.org · 2025-01-31 · 10 citations

  preprintOpen access

  Test-time scaling is a promising new approach to language modeling that uses extra test-time compute to improve performance. Recently, OpenAI's o1 model showed this capability but did not publicly share its methodology, leading to many replication efforts. We seek the simplest approach to achieve test-time scaling and strong reasoning performance. First, we curate a small dataset s1K of 1,000 questions paired with reasoning traces relying on three criteria we validate through ablations: difficul…

  PublisherOA PDFDOI

Recent grants

• RI: Small: Learning to Read, Ground, and Reason in Multimodal Text

  NSF · $450k · 2016–2020

• EAGER: Generating and Understanding Narratives for Dynamic Environments

  NSF · $150k · 2013–2016

Frequent coauthors

• Noah A. Smith

  101 shared

• Luke Zettlemoyer

  90 shared

• Sewon Min

  89 shared

• Yejin Choi

  77 shared

• Daniel Khashabi

  72 shared

• Ali Farhadi

  68 shared

• Akari Asai

  67 shared

• Mari Ostendorf

  43 shared

Education

• Ph.D.

  University of Illinois at Urbana-Champaign

• Other

  Disney Research and CMU

Awards & honors

• 2020 Alfred Sloan Fellowship
• 2021 NSF CAREER award
• 2019 Intel rising star award
• 2018 Allen Distinguished Investigator award
• 2023 Academic Achievement UIUC Alumni award

Similar researchers at University of Washington

• Dieter Foxh-107
• Jeffrey Heerh-80