b'Xuezhi Wang'

research

∙ 05/30/2023

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

Large language models (LLMs) can learn to perform a wide range of natura...

0 Bailin Wang, et al. ∙

research

∙ 05/26/2023

Large Language Models as Tool Makers

Recent research shows the potential of enhancing the problem-solving abi...

7 Tianle Cai, et al. ∙

research

∙ 04/17/2023

Towards Robust Prompts on Vision-Language Models

With the advent of vision-language models (VLMs) that can perform in-con...

0 Jindong Gu, et al. ∙

research

∙ 03/01/2023

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

Despite huge gains in performance in natural language understanding via ...

0 Amanda Baughan, et al. ∙

research

∙ 02/17/2023

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

The limits of open-ended generative models are unclear, yet increasingly...

0 Albert Lu, et al. ∙

research

∙ 11/21/2022

TEMPERA: Test-Time Prompting via Reinforcement Learning

Careful prompt design is critical to the use of large language models in...

0 Tianjun Zhang, et al. ∙

research

∙ 10/28/2022

Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers

Large pre-trained language models have shown remarkable performance over...

2 Jieyu Zhao, et al. ∙

research

∙ 10/20/2022

Large Language Models Can Self-Improve

Large Language Models (LLMs) have achieved excellent performances in var...

1 Jiaxin Huang, et al. ∙

research

∙ 10/06/2022

Language Models are Multilingual Chain-of-Thought Reasoners

We evaluate the reasoning abilities of large language models in multilin...

0 Freda Shi, et al. ∙

research

∙ 10/04/2022

Recitation-Augmented Language Models

We propose a new paradigm to help Large Language Models (LLMs) generate ...

0 Zhiqing Sun, et al. ∙

research

∙ 07/02/2022

Rationale-Augmented Ensembles in Language Models

Recent research has shown that rationales, or step-by-step chains of tho...

1 Xuezhi Wang, et al. ∙

research

∙ 05/21/2022

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

We propose a novel prompting strategy, least-to-most prompting, that ena...

1 Denny Zhou, et al. ∙

research

∙ 04/22/2022

Gravity aided navigation using Viterbi map matching algorithm

In GNSS-denied environments, aiding a vehicle's inertial navigation syst...

0 Wenchao Li, et al. ∙

research

∙ 04/05/2022

PaLM: Scaling Language Modeling with Pathways

Large language models have been shown to achieve remarkable performance ...

6 Aakanksha Chowdhery, et al. ∙

research

∙ 03/31/2022

Probabilistic Map Matching for Robust Inertial Navigation Aiding

Robust aiding of inertial navigation systems in GNSS-denied environments...

0 Xuezhi Wang, et al. ∙

research

∙ 03/21/2022

Self-Consistency Improves Chain of Thought Reasoning in Language Models

We explore a simple ensemble strategy, self-consistency, that significan...

0 Xuezhi Wang, et al. ∙

research

∙ 03/20/2022

Continual Sequence Generation with Adaptive Compositional Modules

Continual learning is essential for real-world deployment when there is ...

0 Yanzhe Zhang, et al. ∙

research

∙ 01/28/2022

Chain of Thought Prompting Elicits Reasoning in Large Language Models

Although scaling up language model size has reliably improved performanc...

9 Jason Wei, et al. ∙

research

∙ 12/15/2021

Measure and Improve Robustness in NLP Models: A Survey

As NLP models achieved state-of-the-art performances over benchmarks and...

0 Xuezhi Wang, et al. ∙

research

∙ 10/15/2021

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

We investigate the robustness of vision transformers (ViTs) through the ...

27 Yao Qin, et al. ∙

research

∙ 10/14/2021

Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models

Recently, NLP models have achieved remarkable progress across a variety ...

0 Tianlu Wang, et al. ∙

research

∙ 04/20/2021

TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution

Single Image Super-resolution (SISR) produces high-resolution images wit...

7 Fayaz Ali Dharejo, et al. ∙

research

∙ 04/12/2021

Continual Learning for Text Classification with Information Disentanglement Based Regularization

Continual learning has become increasingly important as it enables NLP m...

10 Yufan Huang, et al. ∙

research

∙ 02/16/2021

Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Information

Training and evaluation of fair classifiers is a challenging problem. Th...

16 Pranjal Awasthi, et al. ∙

research

∙ 11/06/2020

Underspecification Presents Challenges for Credibility in Modern Machine Learning

ML models often exhibit unexpectedly poor behavior when they are deploye...

30 Alexander D'Amour, et al. ∙

research

∙ 10/12/2020

Measuring and Reducing Gendered Correlations in Pre-trained Models

Pre-trained models have revolutionized natural language understanding. H...

1 Kellie Webster, et al. ∙

research

∙ 10/05/2020

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

NLP models are shown to suffer from robustness issues, i.e., a model's p...

0 Tianlu Wang, et al. ∙

research

∙ 04/29/2020

ToTTo: A Controlled Table-To-Text Generation Dataset

We present ToTTo, an open-domain English table-to-text dataset with over...

0 Ankur P. Parikh, et al. ∙

research

∙ 11/05/2019

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

Most literature in fairness has focused on improving fairness with respe...

23 Xuezhi Wang, et al. ∙

research

∙ 06/24/2019

Transfer of Machine Learning Fairness across Domains

If our models are used in new or unexpected cases, do we know if they wi...

4 Candice Schumann, et al. ∙

research

∙ 04/20/2018

Verifying Text Summaries of Relational Data Sets

We present a novel natural language query interface, the AggChecker, aim...

0 Saehan Jo, et al. ∙

research

∙ 11/25/2015

Maximum Likelihood Estimation for Single Linkage Hierarchical Clustering

We derive a statistical model for estimation of a dendrogram from single...

0 Dekang Zhu, et al. ∙

research

∙ 11/24/2015

Statistical Properties of the Single Linkage Hierarchical Clustering Estimator

Distance-based hierarchical clustering (HC) methods are widely used in u...

0 Dekang Zhu, et al. ∙

Xuezhi Wang

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