---
title: "Hannaneh Hajishirzi — papers and cards"
description: "Hannaneh Hajishirzi: 3 paper cards in the Research library (2014–2023), with what each paper is about, the ideas Mova took from it, and the co-authors."
canonical: "https://research.elamur.ai/free/en/authors/hannaneh-hajishirzi/"
---
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# Hannaneh Hajishirzi

3 paper cards in the Research library · 2014–2023

Hannaneh Hajishirzi wrote 3 of the papers Mova reads, in 2014–2023; often with [Ally Zhang](https://md.research.elamur.ai/free/en/authors/ally-zhang/), Ananth Gottumukkala, Ben Bogin.

[OpenAlex](https://openalex.org/A5082305994) · [ORCID](https://orcid.org/0000-0002-1055-6657) · [**h-index 61**](https://openalex.org/authors/A5082305994) · [17696 citations](https://openalex.org/authors/A5082305994) · [287 works](https://openalex.org/works?filter=author.id:A5082305994) — openalex-snapshot, 2026-10-14

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Hannaneh+Hajishirzi) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Hannaneh+Hajishirzi) · [dblp](https://dblp.org/search?q=Hannaneh+Hajishirzi) · [ACL Anthology](https://aclanthology.org/search/?q=Hannaneh+Hajishirzi) · [ORCID](https://orcid.org/orcid-search/search?searchQuery=Hannaneh+Hajishirzi)

## Papers

1. **[CREPE: Open-Domain Question Answering with False Presuppositions](https://md.research.elamur.ai/free/en/papers/yu-2023-crepe-open-domain-question-answering/)** *(2023)* CREPE has 8,400 «explain like I'm five» questions from Reddit. Annotators, guided by the most upvoted answer, mark whether the question rests on a false presupposition and, if so, write the presupposition and a correction. About 25% of real information-seeking questions contain one. A sample of 50 shows the kinds: a…
2. **[Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/)** *(2020)* `measured` Standard test sets come from the same distribution as the training data, so a dataset with systematic gaps or annotation artifacts lets models score well with shallow heuristics.
3. **[Learning to Solve Arithmetic Word Problems with Verb Categorization](https://md.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/)** *(2014)* The paper presents ARIS, an early system for addition and subtraction word problems that does not depend on equation templates.

## Co-authors (31)

- [Ally Zhang](https://md.research.elamur.ai/free/en/authors/ally-zhang/)
- Ananth Gottumukkala
- Ben Bogin
- Ben Zhou
- [Daniel Khashabi](https://md.research.elamur.ai/free/en/authors/daniel-khashabi/)
- [Dheeru Dua](https://md.research.elamur.ai/free/en/authors/dheeru-dua/)
- Eric Wallace
- Gabriel Ilharco
- [Jiangming Liu](https://md.research.elamur.ai/free/en/authors/jiangming-liu/)
- Jonathan Berant
- Kevin Lin
- [Luke Zettlemoyer](https://md.research.elamur.ai/free/en/authors/luke-zettlemoyer/)
- [Matt Gardner](https://md.research.elamur.ai/free/en/authors/matt-gardner/)
- Mohammad Javad Hosseini
- Nate Kushman
- [Nelson F. Liu](https://md.research.elamur.ai/free/en/authors/nelson-f-liu/)
- Nitish Gupta
- [Noah A. Smith](https://md.research.elamur.ai/free/en/authors/noah-a-smith/)
- Oren Etzioni
- Phoebe Mulcaire
- [Pradeep Dasigi](https://md.research.elamur.ai/free/en/authors/pradeep-dasigi/)
- [Qiang Ning](https://md.research.elamur.ai/free/en/authors/qiang-ning/)
- [Reut Tsarfaty](https://md.research.elamur.ai/free/en/authors/reut-tsarfaty/)
- [Sameer Singh](https://md.research.elamur.ai/free/en/authors/sameer-singh/)
- Sanjay Subramanian
- Sewon Min
- Sihao Chen
- Victoria Basmov
- Xinyan Yu
- Yanai Elazar
- [Yoav Artzi](https://md.research.elamur.ai/free/en/authors/yoav-artzi/)

## More papers in the library (33)

Pages of the Research library: our summary, the ideas, references and who cites each.

- [s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/) — Niklas Muennighoff, Zitong Yang, Weijia Shi, et al. · 2025
- [Data Engineering for Scaling Language Models to 128K Context](https://md.research.elamur.ai/free/en/papers/fu-2024-data-engineering-scaling-language-models/) — Yao Fu, Rameswar Panda, Xinyao Niu, et al. · 2024
- [Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2](https://md.research.elamur.ai/free/en/papers/ivison-2023-camels-changing-climate-enhancing-lm/) — Hamish Ivison, Yizhong Wang, Valentina Pyatkin, et al. · 2023
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/) — Sewon Min, Kalpesh Krishna, Xinxi Lyu, et al. · 2023
- [Fine-Grained Human Feedback Gives Better Rewards for Language Model Training](https://md.research.elamur.ai/free/en/papers/wu-2023-fine-grained-human-feedback-gives/) — Zeqiu Wu, Yushi Hu, Weijia Shi, et al. · 2023
- [How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://md.research.elamur.ai/free/en/papers/wang-2023-far-camels-go-exploring-state/) — Yizhong Wang, Hamish Ivison, Pradeep Dasigi, et al. · 2023
- [MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts](https://md.research.elamur.ai/free/en/papers/lu-2023-mathvista-evaluating-mathematical-reasoning-foundation/) — Pan Lu, Hritik Bansal, Tony Xia, et al. · 2023
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://md.research.elamur.ai/free/en/papers/asai-2023-self-rag-learning-retrieve-generate/) — Akari Asai, Zeqiu Wu, Yizhong Wang, et al. · 2023
- [Task-aware Retrieval with Instructions](https://md.research.elamur.ai/free/en/papers/asai-2023-task-aware-retrieval-instructions/) — Akari Asai, Timo Schick, Patrick Lewis, et al. · 2023
- [Editing Models with Task Arithmetic](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/) — Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al. · 2022
- [Evidentiality-guided Generation for Knowledge-Intensive NLP Tasks](https://md.research.elamur.ai/free/en/papers/asai-2022-evidentiality-guided-generation-knowledge-intensive/) — Akari Asai, Matt Gardner, Hannaneh Hajishirzi · 2022
- [Is Reinforcement Learning (Not) for Natural Language Processing: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization](https://md.research.elamur.ai/free/en/papers/ramamurthy-2022-reinforcement-learning-natural-language-processing/) — Rajkumar Ramamurthy, Prithviraj Ammanabrolu, Kiante Brantley, et al. · 2022
- [Reframing Instructional Prompts to GPTk's Language](https://md.research.elamur.ai/free/en/papers/mishra-2022-reframing-instructional-prompts-gptk-s/) — Swaroop Mishra, Daniel Khashabi, Chitta Baral, et al. · 2022
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/) — Sewon Min, Xinxi Lyu, Ari Holtzman, et al. · 2022
- [Noisy Channel Language Model Prompting for Few-Shot Text Classification](https://md.research.elamur.ai/free/en/papers/min-2021-noisy-channel-language-model-prompting/) — Sewon Min, Mike Lewis, Hannaneh Hajishirzi, et al. · 2021
- [Probing Contextual Language Models for Common Ground with Visual Representations](https://md.research.elamur.ai/free/en/papers/ilharco-2021-probing-contextual-language-models-common/) — Gabriel Ilharco, Rowan Zellers, Ali Farhadi, et al. · 2021
- [XOR QA: Cross-lingual Open-Retrieval Question Answering](https://md.research.elamur.ai/free/en/papers/asai-2021-xor-qa-cross-lingual-open/) — Akari Asai, Jungo Kasai, Jonathan Clark, et al. · 2021
- [Fact or Fiction: Verifying Scientific Claims](https://md.research.elamur.ai/free/en/papers/wadden-2020-fact-or-fiction-verifying-scientific/) — David Wadden, Shanchuan Lin, Kyle Lo, et al. · 2020
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://md.research.elamur.ai/free/en/papers/jain-2020-scirex-challenge-dataset-document-level/) — Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi, et al. · 2020
- [UNIFIEDQA: Crossing Format Boundaries with a Single QA System](https://md.research.elamur.ai/free/en/papers/khashabi-2020-unifiedqa-crossing-format-boundaries-single/) — Daniel Khashabi, Sewon Min, Tushar Khot, et al. · 2020
- [A Discrete Hard EM Approach for Weakly Supervised Question Answering](https://md.research.elamur.ai/free/en/papers/min-2019-discrete-hard-em-approach-weakly/) — Sewon Min, Danqi Chen, Hannaneh Hajishirzi, et al. · 2019
- [A general framework for information extraction using dynamic span graphs](https://md.research.elamur.ai/free/en/papers/luan-2019-general-framework-information-extraction-dynamic/) — Yi Luan, Dave Wadden, Luheng He, et al. · 2019
- [Compositional Questions Do Not Necessitate Multi-hop Reasoning](https://md.research.elamur.ai/free/en/papers/min-2019-compositional-questions-do-not-necessitate/) — Sewon Min, Eric Wallace, Sameer Singh, et al. · 2019
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://md.research.elamur.ai/free/en/papers/wadden-2019-entity-relation-event-extraction-contextualized/) — David Wadden, Ulme Wennberg, Yi Luan, et al. · 2019
- [Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering](https://md.research.elamur.ai/free/en/papers/asai-2019-learning-retrieve-reasoning-paths-over/) — Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, et al. · 2019
- [MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms](https://md.research.elamur.ai/free/en/papers/amini-2019-mathqa-interpretable-math-word-problem/) — Aida Amini, Saadia Gabriel, Shanchuan Lin, et al. · 2019
- [Multi-hop Reading Comprehension through Question Decomposition and Rescoring](https://md.research.elamur.ai/free/en/papers/min-2019-multi-hop-reading-comprehension-through/) — Sewon Min, Victor Zhong, Luke Zettlemoyer, et al. · 2019
- [Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index](https://md.research.elamur.ai/free/en/papers/seo-2019-real-time-open-domain-question/) — Minjoon Seo, Jinhyuk Lee, Tom Kwiatkowski, et al. · 2019
- [Text Generation from Knowledge Graphs with Graph Transformers](https://md.research.elamur.ai/free/en/papers/kedziorski-2019-text-generation-knowledge-graphs-graph/) — Rik Koncel-Kedziorski, Dhanush Bekal, Yi Luan, et al. · 2019
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/luan-2018-multi-task-identification-entities-relations/) — Yi Luan, Luheng He, Mari Ostendorf, et al. · 2018
- [A Diagram Is Worth A Dozen Images](https://md.research.elamur.ai/free/en/papers/kembhavi-2016-diagram-worth-dozen-images/) — Aniruddha Kembhavi, Mike Salvato, Eric Kolve, et al. · 2016
- [Bidirectional Attention Flow for Machine Comprehension](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/) — Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, et al. · 2016
- [Joint Coreference Resolution and Named-Entity Linking with Multi-Pass Sieves](https://md.research.elamur.ai/free/en/papers/hajishirzi-2013-joint-coreference-resolution-named-entity/) — Hannaneh Hajishirzi, Leila Zilles, Daniel S. Weld, et al. · 2013