Xuezhi Wang

Orcid: 0000-0003-3299-2780

Affiliations:

Google Research, New York, NY, USA
Carnegie Mellon University, Computer Science Department, Pittsburgh, PA, USA (former)

According to our database¹, Xuezhi Wang authored at least 67 papers between 2011 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

Scaling Instruction-Finetuned Language Models.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2024

A Minimalist Prompt for Zero-Shot Policy Learning.

[BibT_eX]

[DOI]

CoRR, 2024

Chain-of-Thought Reasoning Without Prompting.

[BibT_eX]

[DOI]

Xuezhi Wang

Denny Zhou

CoRR, 2024

Transformers Can Achieve Length Generalization But Not Robustly.

[BibT_eX]

[DOI]

CoRR, 2024

Premise Order Matters in Reasoning with Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Large Language Models as Optimizers.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Large Language Models as Tool Makers.

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023

PaLM: Scaling Language Modeling with Pathways.

[BibT_eX]

[DOI]

Vinodkumar Prabhakaran

Thanumalayan Sankaranarayana Pillai

Kathy Meier-Hellstern

J. Mach. Learn. Res., 2023

Universal Self-Consistency for Large Language Model Generation.

[BibT_eX]

[DOI]

CoRR, 2023

Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals.

[BibT_eX]

[DOI]

CoRR, 2023

Towards Robust Prompts on Vision-Language Models.

[BibT_eX]

[DOI]

CoRR, 2023

What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel.

[BibT_eX]

[DOI]

Yao Qin

Xuezhi Wang

Balaji Lakshminarayanan

Ed H. Chi

Alex Beutel

Proceedings of the 2023 IEEE Conference on Secure and Trustworthy Machine Learning, 2023

Grammar Prompting for Domain-Specific Language Generation with Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

TEMPERA: Test-Time Prompt Editing via Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

UL2: Unifying Language Learning Paradigms.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Recitation-Augmented Language Models.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Language models are multilingual chain-of-thought reasoners.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Self-Consistency Improves Chain of Thought Reasoning in Language Models.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Large Language Models Can Self-Improve.

[BibT_eX]

[DOI]

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: EACL 2023, 2023

A Mixed-Methods Approach to Understanding User Trust after Voice Assistant Failures.

[BibT_eX]

[DOI]

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023

2022

Underspecification Presents Challenges for Credibility in Modern Machine Learning.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2022

TEMPERA: Test-Time Prompting via Reinforcement Learning.

[BibT_eX]

[DOI]

CoRR, 2022

Scaling Instruction-Finetuned Language Models.

[BibT_eX]

[DOI]

CoRR, 2022

Rationale-Augmented Ensembles in Language Models.

[BibT_eX]

[DOI]

CoRR, 2022

Least-to-Most Prompting Enables Complex Reasoning in Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2022

Self-Consistency Improves Chain of Thought Reasoning in Language Models.

[BibT_eX]

[DOI]

CoRR, 2022

Chain of Thought Prompting Elicits Reasoning in Large Language Models.

[BibT_eX]

[DOI]

CoRR, 2022

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation.

[BibT_eX]

[DOI]

Yao Qin

Chiyuan Zhang

Ting Chen

Balaji Lakshminarayanan

Alex Beutel

Xuezhi Wang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: NAACL 2022, 2022

Measure and Improve Robustness in NLP Models: A Survey.

[BibT_eX]

[DOI]

Xuezhi Wang

Haohan Wang

Diyi Yang

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2022

Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers.

[BibT_eX]

[DOI]

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

Continual Sequence Generation with Adaptive Compositional Modules.

[BibT_eX]

[DOI]

Yanzhe Zhang

Xuezhi Wang

Diyi Yang

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022

2021

Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models.

[BibT_eX]

[DOI]

Tianlu Wang

Diyi Yang

Xuezhi Wang

CoRR, 2021

Measuring Recommender System Effects with Simulated Users.

[BibT_eX]

[DOI]

CoRR, 2021

Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems.

[BibT_eX]

[DOI]

Proceedings of the WSDM '21, 2021

Improving Calibration through the Relationship with Adversarial Robustness.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Continual Learning for Text Classification with Information Disentanglement Based Regularization.

[BibT_eX]

[DOI]

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning.

[BibT_eX]

[DOI]

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information.

[BibT_eX]

[DOI]

Pranjal Awasthi

Alex Beutel

Matthäus Kleindessner

Jamie Morgenstern

Xuezhi Wang

Proceedings of the FAccT '21: 2021 ACM Conference on Fairness, 2021

Can We Improve Model Robustness through Secondary Attribute Counterfactuals?

[BibT_eX]

[DOI]

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021

Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective.

[BibT_eX]

[DOI]

Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020

Measuring and Reducing Gendered Correlations in Pre-trained Models.

[BibT_eX]

[DOI]

CoRR, 2020

Improving Uncertainty Estimates through the Relationship with Adversarial Robustness.

[BibT_eX]

[DOI]

CoRR, 2020

Fairness without Demographics through Adversarially Reweighted Learning.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation.

[BibT_eX]

[DOI]

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

ToTTo: A Controlled Table-To-Text Generation Dataset.

[BibT_eX]

[DOI]

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

2019

Mining an "Anti-Knowledge Base" from Wikipedia Updates with Applications to Fact Checking and Beyond.

[BibT_eX]

[DOI]

Proc. VLDB Endow., 2019

AggChecker: A Fact-Checking System for Text Summaries of Relational Data Sets.

[BibT_eX]

[DOI]

Proc. VLDB Endow., 2019

Practical Compositional Fairness: Understanding Fairness in Multi-Task ML Systems.

[BibT_eX]

[DOI]

CoRR, 2019

Transfer of Machine Learning Fairness across Domains.

[BibT_eX]

[DOI]

CoRR, 2019

Automatically Generating Interesting Facts from Wikipedia Tables.

[BibT_eX]

[DOI]

Proceedings of the 2019 International Conference on Management of Data, 2019

Verifying Text Summaries of Relational Data Sets.

[BibT_eX]

[DOI]

Proceedings of the 2019 International Conference on Management of Data, 2019

Summarizing News Articles Using Question-and-Answer Pairs via Learning.

[BibT_eX]

[DOI]

Xuezhi Wang

Cong Yu

Proceedings of the Semantic Web - ISWC 2019, 2019

Contextual Fact Ranking and Its Applications in Table Synthesis and Compression.

[BibT_eX]

[DOI]

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

2018

Relevant Document Discovery for Fact-Checking Articles.

[BibT_eX]

[DOI]

Proceedings of the Companion of the The Web Conference 2018 on The Web Conference 2018, 2018

2016

Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

2015

Generalization Bounds for Transfer Learning under Model Shift.

[BibT_eX]

[DOI]

Xuezhi Wang

Jeff G. Schneider

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

2014

Biperpedia: An Ontology for Search Applications.

[BibT_eX]

[DOI]

Proc. VLDB Endow., 2014

Flexible Transfer Learning under Support and Model Shift.

[BibT_eX]

[DOI]

Xuezhi Wang

Jeff G. Schneider

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Active Transfer Learning under Model Shift.

[BibT_eX]

[DOI]

Xuezhi Wang

Tzu-Kuo Huang

Jeff G. Schneider

Proceedings of the 31th International Conference on Machine Learning, 2014

2013

Active search on graphs.

[BibT_eX]

[DOI]

Xuezhi Wang

Roman Garnett

Jeff G. Schneider

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

2011

ADANA: Active Name Disambiguation.

[BibT_eX]

[DOI]

Proceedings of the 11th IEEE International Conference on Data Mining, 2011

Xuezhi Wang

Timeline

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