Research Themes
My research is motivated by understanding the computational foundations of intelligent behavior, often through the lens of natural language.
I am excited about intelligence amplification — building computational models that would augment human experience in a mutually interdependent fashion.
The dominant majority of my research is aligned with natural language processing (ACL, NAACL, EMNLP), machine learning (ICLR, NeurIPS, ICML) and artificial intelligence (AAAI, IJCAI).
Here are several themes I am interested in:
General-purpose models:
AlphaGo may be the world champion at Go, although it can't solve any other problem! How can we build models that generalize a broader scope of tasks, abilities, modalities, or environments?
Self-supervised representation learning:
The AI literature has found powerful ways to build rich representations of the world by utilizing cheap signals available in the wild (web data, physical environment, etc.).
How can we make these algorithms more effective and efficient (in terms of data or computation cost)?
How can we make them robust to distributional drifts in data, e.g., low-data regimes or adversarial settings?
How can we scale them up to various modalities or forms of communication/interaction?
Reasoning and problem-solving: I view “reasoning” as the process of using “reasons” to explain or justify decisions.
How can we enable machines to communicate via reasons, for a broad-ranging spectrum of tasks?
How can we make this process “verifiable” or “explainable” to humans?
Can we build systems that can recourse upon a mistake?
Interaction, communication, and coordination: Can we engineer AI systems to effectively engage, interact, and communicate with humans and other AI systems for the purpose of, for example, coordination?
AI ↔ humans: The ultimate goal of our work is to benefit humans! How should we engineer the interface between AI and machines?
What forms of interaction are most effective and meaningful for humans? How can we make AI systems more transparent and accountable to humans? Can we turn such transparency into a truly democratic oversight of systems, their algorithmic biases and mistakes? How should we think about personalizing AI systems to their users?
If you are an undergraduate or masters student and would like to work on research with my group, please fill out this form.
Select Talks
Oracle Labs ML seminar series, The Uphill Battle of Making Language Models Reliable, 2024.
New York University, Large Language Models: Revisiting Few Mysteries, 2023.
University of Maryland, Ailments of Alignment: Hurdles in Adapting Large Language Models to Follow Human Demands, 2023.
Johns Hopkins University CLSP, The Quest Toward Generality in Natural Language Understanding, 2022.
Google AI, Broadening the Scope of Machine Comprehension, 2021.
Carnegie Mellon University, Natural Language Understanding with Indirect Supervision, 2019.
Mid-Atlantic Student Colloquium on Speech, Language and Learning, Abductive Reasoning on Natural Language Questions as
Global Reasoning over Semantic Abstractions, 2018.
Teaching
Publication
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Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adri`{a} Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlm"{u}ller, Andrew Dai, Andrew La, Andrew Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta and others. Transactions on Machine Learning Research (TMLR), 2023. [data]
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ Tasks. Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, Eshaan Pathak, Giannis Karamanolakis, Haizhi Gary Lai, Ishan Purohit, Ishani Mondal, Jacob Anderson, Kirby Kuznia, Krima Doshi, Maitreya Patel, Kuntal Kumar Pal, Mehrad Moradshahi, Mihir Parmar, Mirali Purohit, Neeraj Varshney, Phani Rohitha Kaza, Pulkit Verma, Ravsehaj Singh Puri, Rushang Karia, Shailaja Keyur Sampat, Savan Doshi, Siddhartha Mishra, Sujan Reddy, Sumanta Patro, Tanay Dixit, Xudong Shen, Chitta Baral, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi and Daniel Khashabi. Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. [data] [slides] [slides2] [poster] [project] [blog]
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics. Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith and Yejin Choi. Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022. Best paper award. [code]
Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts. Daniel Khashabi, Xinxi Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, Sameer Singh and Yejin Choi. Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022. [slides] [slides2] [talk] [code]
Findings of the 2021 Conference on Machine Translation (WMT21). Farhad Akhbardeh, Arkady Arkhangorodsky, Magdalena Biesialska, Ond{v{r}}ej Bojar, Rajen Chatterjee, Vishrav Chaudhary, Marta R. Costa-jussa, Cristina Espa{~n}a-Bonet, Angela Fan, Christian Federmann, Markus Freitag, Yvette Graham, Roman Grundkiewicz, Barry Haddow, Leonie Harter, Kenneth Heafield, Christopher Homan, Matthias Huck, Kwabena Amponsah-Kaakyire, Jungo Kasai, Daniel Khashabi, Kevin Knight, Tom Kocmi, Philipp Koehn, Nicholas Lourie, Christof Monz, Makoto Morishita, Masaaki Nagata, Ajay Nagesh, Toshiaki Nakazawa, Matteo Negri, Santanu Pal, Allahsera Auguste Tapo, Marco Turchi, Valentin Vydrin and Marcos Zampieri. Conference on Machine Translation (WMT), 2021.
ParsiNLU: A Suite of Language Understanding Challenges for Persian. Daniel Khashabi, Arman Cohan, Siamak Shakeri, Pedram Hosseini, Pouya Pezeshkpour, Malihe Alikhani, Moin Aminnaseri, Marzieh Bitaab, Faeze Brahman, Sarik Ghazarian and others. Transactions of the Association for Computational Linguistics (TACL), 2021. [slides] [code]
From ‘F’ to ‘A’ on the NY Regents Science Exams: An Overview of the Aristo Project. Peter Clark, Oren Etzioni, Daniel Khashabi, Tushar Khot, Bhavana Dalvi Mishra, Kyle Richardson, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, Niket Tandon, Sumithra Bhakthavatsalam, Dirk Groeneveld, Michal Guerquin and Michael Schmitz. AI Magazine, 2020. [talk] [coverage]
CogCompNLP: Your swiss army knife for nlp. Daniel “Khashabi, Mark Sammons, Ben Zhou, Tom Redman, Christos Christodoulopoulos, Vivek Srikumar, Nicholas Rizzolo, Lev Ratinov, Guanheng Luo, Quang Do, Chen-Tse Tsai, Subhro Roy, Stephen Mayhew, Zhili Feng, John Wieting, Xiaodong Yu, Yangqiu Song, Shashank Gupta, Shyam Upadhyay, Naveen Arivazhagan, Qiang Ning, Shaoshi Ling and Dan” Roth. International Conference on Language Resources and Evaluation (LREC), 2018. [poster] [code]
Image demosaicing. Reinhard Sebastian Bernhard Nowozin, Danyal Khashabi, Jeremy Martin Jancsary, Bruce Justin Lindbloom and Andrew William Fitzgibbon. US Patent 9,344,690 - Google Patents, 2016.
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