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ORCIDKyle Richardson 0001
Kyle D. Richardson
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- affiliation: Allen Institute for AI, Seattle, WA, USA
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Books and Theses
- 2018
[b1]Kyle Richardson:
New resources and ideas for semantic parser induction. University of Stuttgart, Germany, 2018
Journal Articles
- 2022
- [j4]Gregor Betz, Kyle Richardson:
Judgment aggregation, discursive dilemma and reflective equilibrium: Neural language models as self-improving doxastic agents. Frontiers Artif. Intell. 5 (2022) - 2020
- [j3]Peter Clark, Oren Etzioni, Tushar Khot, Daniel Khashabi, Bhavana Dalvi Mishra, Kyle Richardson, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, Niket Tandon, Sumithra Bhakthavatsalam, Dirk Groeneveld, Michal Guerquin, Michael Schmitz:
From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project. AI Mag. 41(4): 39-53 (2020) - [j2]Kyle Richardson, Ashish Sabharwal:
What Does My QA Model Know? Devising Controlled Probes using Expert. Trans. Assoc. Comput. Linguistics 8: 572-588 (2020) - 2016
- [j1]Kyle D. Richardson, Jonas Kuhn:
Learning to Make Inferences in a Semantic Parsing Task. Trans. Assoc. Comput. Linguistics 4: 155-168 (2016)
Conference and Workshop Papers
- 2023
- [c31]Zeming Chen
, Qiyue Gao, Antoine Bosselut, Ashish Sabharwal, Kyle Richardson:
DISCO: Distilling Counterfactuals with Large Language Models. ACL (1) 2023: 5514-5528 - [c30]Nora Kassner, Oyvind Tafjord, Ashish Sabharwal, Kyle Richardson, Hinrich Schütze, Peter Clark:
Language Models with Rationality. EMNLP 2023: 14190-14201 - [c29]Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, Ashish Sabharwal:
Decomposed Prompting: A Modular Approach for Solving Complex Tasks. ICLR 2023 - 2022
- [c28]Kyle Richardson, Ashish Sabharwal:
Pushing the Limits of Rule Reasoning in Transformers through Natural Language Satisfiability. AAAI 2022: 11209-11219 - [c27]Tushar Khot, Kyle Richardson, Daniel Khashabi, Ashish Sabharwal:
Hey AI, Can You Solve Complex Tasks by Talking to Agents? ACL (Findings) 2022: 1808-1823 - [c26]Matthew Finlayson, Kyle Richardson, Ashish Sabharwal, Peter Clark:
What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment. EMNLP 2022: 414-426 - [c25]Ben Zhou, Kyle Richardson, Xiaodong Yu, Dan Roth:
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts. EMNLP 2022: 2223-2235 - [c24]Kyle Richardson, Ronen Tamari, Oren Sultan, Dafna Shahaf, Reut Tsarfaty, Ashish Sabharwal:
Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs. EMNLP 2022: 9703-9719 - [c23]Daniel Khashabi, Xinxi Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, Sameer Singh, Yejin Choi:
Prompt Waywardness: The Curious Case of Discretized Interpretation of Continuous Prompts. NAACL-HLT 2022: 3631-3643 - [c22]Gregor Betz, Kyle Richardson:
DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models. *SEM@NAACL-HLT 2022: 12-27 - [c21]Ronen Tamari, Kyle Richardson, Noam Kahlon, Aviad Sar-Shalom, Nelson F. Liu, Reut Tsarfaty, Dafna Shahaf:
Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic benchmarking. *SEM@NAACL-HLT 2022: 101-122 - 2021
- [c20]Hai Hu, He Zhou, Zuoyu Tian, Yiwen Zhang, Yina Patterson, Yanting Li, Yixin Nie, Kyle Richardson:
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference. ACL/IJCNLP (Findings) 2021: 3770-3785 - [c19]Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, Ashish Sabharwal:
Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models. NAACL-HLT 2021: 1264-1279 - [c18]Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, Dan Roth:
Temporal Reasoning on Implicit Events from Distant Supervision. NAACL-HLT 2021: 1361-1371 - 2020
- [c17]Kyle Richardson, Hai Hu, Lawrence S. Moss, Ashish Sabharwal:
Probing Natural Language Inference Models through Semantic Fragments. AAAI 2020: 8713-8721 - [c16]Atticus Geiger, Kyle Richardson, Christopher Potts:
Neural Natural Language Inference Models Partially Embed Theories of Lexical Entailment and Negation. BlackboxNLP@EMNLP 2020: 163-173 - [c15]Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan:
CLUE: A Chinese Language Understanding Evaluation Benchmark. COLING 2020: 4762-4772 - [c14]Hai Hu, Kyle Richardson, Liang Xu, Lu Li, Sandra Kübler, Lawrence S. Moss:
OCNLI: Original Chinese Natural Language Inference. EMNLP (Findings) 2020: 3512-3526 - [c13]Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, Eduard H. Hovy:
A Dataset for Tracking Entities in Open Domain Procedural Text. EMNLP (1) 2020: 6408-6417 - [c12]Peter Clark, Oyvind Tafjord, Kyle Richardson:
Transformers as Soft Reasoners over Language. IJCAI 2020: 3882-3890 - 2018
- [c11]Kyle Richardson, Jonathan Berant, Jonas Kuhn:
Polyglot Semantic Parsing in APIs. NAACL-HLT 2018: 720-730 - 2017
- [c10]Kyle Richardson, Jonas Kuhn:
Learning Semantic Correspondences in Technical Documentation. ACL (1) 2017: 1612-1622 - [c9]Kyle Richardson, Jonas Kuhn:
Function Assistant: A Tool for NL Querying of APIs. EMNLP (System Demonstrations) 2017: 67-72 - [c8]Kyle Richardson, Sina Zarrieß, Jonas Kuhn:
The Code2Text Challenge: Text Generation in Source Libraries. INLG 2017: 115-119 - 2015
- [c7]Cleo Condoravdi, Kyle Richardson, Vishal Sikka, Asuman Suenbuel, Richard Waldinger:
Natural Language Access to Data: It Takes Common Sense! AAAI Spring Symposia 2015 - 2014
- [c6]Kyle Richardson, Jonas Kuhn:
UnixMan Corpus: A Resource for Language Learning in the Unix Domain. LREC 2014: 2985-2989 - 2013
- [c5]Sina Zarrieß, Kyle Richardson:
An Automatic Method for Building a Data-to-Text Generator. ENLG 2013: 202-203 - 2012
- [c4]Kyle Richardson, Jonas Kuhn:
Light Textual Inference for Semantic Parsing. COLING (Posters) 2012: 1007-1018 - 2011
- [c3]Richard J. Waldinger, Daniel G. Bobrow, Cleo Condoravdi, Kyle Richardson, Amar Das:
Accessing Structured Health Information through English Queries and Automatic Deduction. AAAI Spring Symposium: AI and Health Communication 2011 - [c2]Daniel G. Bobrow, Cleo Condoravdi, Kyle Richardson, Richard J. Waldinger, Amar Das:
Deducing answers to english questions from structured data. IUI 2011: 299-302 - [c1]Kyle Richardson, Daniel G. Bobrow, Cleo Condoravdi, Richard J. Waldinger, Amar Das:
English Access to Structured Data. ICSC 2011: 13-20
Informal and Other Publications
- 2024
- [i37]Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk, David Atkinson, Russell Authur, Ben Bogin, Khyathi Raghavi Chandu, Jennifer Dumas, Yanai Elazar, Valentin Hofmann, Ananya Harsh Jha, Sachin Kumar, Li Lucy, Xinxi Lyu, Nathan Lambert, Ian Magnusson, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell, Nishant Subramani, Oyvind Tafjord, Pete Walsh, Luke Zettlemoyer, Noah A. Smith, Hannaneh Hajishirzi, Iz Beltagy, Dirk Groeneveld, Jesse Dodge, Kyle Lo:
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research. CoRR abs/2402.00159 (2024) - [i36]Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyathi Raghavi Chandu, Arman Cohan, Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep Dasigi, Nathan Lambert, Kyle Richardson, Luke Zettlemoyer, Jesse Dodge, Kyle Lo, Luca Soldaini, Noah A. Smith, Hannaneh Hajishirzi:
OLMo: Accelerating the Science of Language Models. CoRR abs/2402.00838 (2024) - [i35]Yikai Zhang, Siyu Yuan, Caiyu Hu, Kyle Richardson, Yanghua Xiao, Jiangjie Chen:
TimeArena: Shaping Efficient Multitasking Language Agents in a Time-Aware Simulation. CoRR abs/2402.05733 (2024) - [i34]Ruihan Yang, Jiangjie Chen, Yikai Zhang, Siyu Yuan, Aili Chen, Kyle Richardson, Yanghua Xiao, Deqing Yang:
SelfGoal: Your Language Agents Already Know How to Achieve High-level Goals. CoRR abs/2406.04784 (2024) - 2023
- [i33]Nora Kassner, Oyvind Tafjord, Ashish Sabharwal, Kyle Richardson, Hinrich Schütze, Peter Clark:
Language Models with Rationality. CoRR abs/2305.14250 (2023) - [i32]Jiangjie Chen, Siyu Yuan, Rong Ye, Bodhisattwa Prasad Majumder, Kyle Richardson:
Put Your Money Where Your Mouth Is: Evaluating Strategic Planning and Execution of LLM Agents in an Auction Arena. CoRR abs/2310.05746 (2023) - [i31]Dirk Groeneveld, Anas Awadalla, Iz Beltagy, Akshita Bhagia, Ian Magnusson, Hao Peng, Oyvind Tafjord, Pete Walsh, Kyle Richardson, Jesse Dodge:
Catwalk: A Unified Language Model Evaluation Framework for Many Datasets. CoRR abs/2312.10253 (2023) - [i30]Ian Magnusson, Akshita Bhagia, Valentin Hofmann, Luca Soldaini, Ananya Harsh Jha, Oyvind Tafjord, Dustin Schwenk, Evan Pete Walsh, Yanai Elazar, Kyle Lo, Dirk Groeneveld, Iz Beltagy, Hannaneh Hajishirzi, Noah A. Smith, Kyle Richardson, Jesse Dodge:
Paloma: A Benchmark for Evaluating Language Model Fit. CoRR abs/2312.10523 (2023) - 2022
- [i29]Matthew Finlayson, Kyle Richardson, Ashish Sabharwal, Peter Clark:
What Makes Instruction Learning Hard? An Investigation and a New Challenge in a Synthetic Environment. CoRR abs/2204.09148 (2022) - [i28]Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, Ashish Sabharwal:
Decomposed Prompting: A Modular Approach for Solving Complex Tasks. CoRR abs/2210.02406 (2022) - [i27]Ben Zhou, Kyle Richardson, Xiaodong Yu, Dan Roth:
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts. CoRR abs/2210.16865 (2022) - [i26]Kyle Richardson, Ronen Tamari, Oren Sultan, Reut Tsarfaty, Dafna Shahaf, Ashish Sabharwal:
Breakpoint Transformers for Modeling and Tracking Intermediate Beliefs. CoRR abs/2211.07950 (2022) - [i25]Zeming Chen, Qiyue Gao, Kyle Richardson, Antoine Bosselut, Ashish Sabharwal:
DISCO: Distilling Phrasal Counterfactuals with Large Language Models. CoRR abs/2212.10534 (2022) - 2021
- [i24]Sumithra Bhakthavatsalam, Daniel Khashabi, Tushar Khot, Bhavana Dalvi Mishra, Kyle Richardson, Ashish Sabharwal, Carissa Schoenick, Oyvind Tafjord, Peter Clark:
Think you have Solved Direct-Answer Question Answering? Try ARC-DA, the Direct-Answer AI2 Reasoning Challenge. CoRR abs/2102.03315 (2021) - [i23]Gregor Betz, Kyle Richardson, Christian Voigt:
Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2. CoRR abs/2103.13033 (2021) - [i22]Hai Hu, He Zhou, Zuoyu Tian, Yiwen Zhang, Yina Ma, Yanting Li, Yixin Nie, Kyle Richardson:
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference. CoRR abs/2106.03983 (2021) - [i21]Gregor Betz, Kyle Richardson:
DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models. CoRR abs/2110.01509 (2021) - [i20]Tushar Khot, Kyle Richardson, Daniel Khashabi, Ashish Sabharwal:
Learning to Solve Complex Tasks by Talking to Agents. CoRR abs/2110.08542 (2021) - [i19]Ronen Tamari, Kyle Richardson, Aviad Sar-Shalom, Noam Kahlon, Nelson F. Liu, Reut Tsarfaty, Dafna Shahaf:
Dyna-bAbI: unlocking bAbI's potential with dynamic synthetic benchmarking. CoRR abs/2112.00086 (2021) - [i18]Daniel Khashabi, Shane Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sameer Singh, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, Yejin Choi:
PROMPT WAYWARDNESS: The Curious Case of Discretized Interpretation of Continuous Prompts. CoRR abs/2112.08348 (2021) - [i17]Kyle Richardson, Ashish Sabharwal:
Pushing the Limits of Rule Reasoning in Transformers through Natural Language Satisfiability. CoRR abs/2112.09054 (2021) - 2020
- [i16]Peter Clark, Oyvind Tafjord, Kyle Richardson:
Transformers as Soft Reasoners over Language. CoRR abs/2002.05867 (2020) - [i15]Atticus Geiger, Kyle Richardson, Christopher Potts:
Modular Representation Underlies Systematic Generalization in Neural Natural Language Inference Models. CoRR abs/2004.14623 (2020) - [i14]Sumithra Bhakthavatsalam, Kyle Richardson, Niket Tandon, Peter Clark:
Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations. CoRR abs/2006.07510 (2020) - [i13]Tushar Khot, Daniel Khashabi, Kyle Richardson, Peter Clark, Ashish Sabharwal:
Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models. CoRR abs/2009.00751 (2020) - [i12]Hai Hu, Kyle Richardson, Liang Xu, Lu Li, Sandra Kübler, Lawrence S. Moss:
OCNLI: Original Chinese Natural Language Inference. CoRR abs/2010.05444 (2020) - [i11]Ben Zhou, Kyle Richardson, Qiang Ning, Tushar Khot, Ashish Sabharwal, Dan Roth:
Temporal Reasoning on Implicit Events from Distant Supervision. CoRR abs/2010.12753 (2020) - [i10]Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi Mishra, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, Eduard H. Hovy:
A Dataset for Tracking Entities in Open Domain Procedural Text. CoRR abs/2011.08092 (2020) - 2019
- [i9]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, Michael Schmitz:
From 'F' to 'A' on the N.Y. Regents Science Exams: An Overview of the Aristo Project. CoRR abs/1909.01958 (2019) - [i8]Kyle Richardson, Hai Hu, Lawrence S. Moss, Ashish Sabharwal:
Probing Natural Language Inference Models through Semantic Fragments. CoRR abs/1909.07521 (2019) - [i7]Hai Hu, Qi Chen, Kyle Richardson, Atreyee Mukherjee, Lawrence S. Moss, Sandra Kübler:
MonaLog: a Lightweight System for Natural Language Inference Based on Monotonicity. CoRR abs/1910.08772 (2019) - [i6]Kyle Richardson, Ashish Sabharwal:
What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge. CoRR abs/1912.13337 (2019) - 2018
- [i5]Kyle Richardson, Jonathan Berant, Jonas Kuhn:
Polyglot Semantic Parsing in APIs. CoRR abs/1803.06966 (2018) - [i4]Kyle Richardson:
A Language for Function Signature Representations. CoRR abs/1804.00987 (2018) - 2017
- [i3]Kyle Richardson, Jonas Kuhn:
Learning Semantic Correspondences in Technical Documentation. CoRR abs/1705.04815 (2017) - [i2]Kyle Richardson, Jonas Kuhn:
Function Assistant: A Tool for NL Querying of APIs. CoRR abs/1706.00468 (2017) - [i1]Kyle Richardson, Sina Zarrieß, Jonas Kuhn:
The Code2Text Challenge: Text Generation in Source Code Libraries. CoRR abs/1708.00098 (2017)
Coauthor Index
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