Shi Feng

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University of Maryland

Calibrate Before Use: Improving Few-Shot Performance of Language Models

GPT-3 can perform numerous tasks when provided a natural language prompt...
02/19/2021 ∙ by Tony Z. Zhao, et al. ∙ 6 ∙ share

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A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training

We investigate response selection for multi-turn conversation in retriev...
12/21/2020 ∙ by Yongkang Liu, et al. ∙ 0 ∙ share

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Customizing Triggers with Concealed Data Poisoning

Adversarial attacks alter NLP model predictions by perturbing test-time ...
10/23/2020 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Universal Adversarial Triggers for Attacking and Analyzing NLP

Adversarial examples highlight model vulnerabilities and are useful for ...
08/20/2019 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Universal Adversarial Triggers for NLP

Adversarial examples highlight model vulnerabilities and are useful for ...
08/20/2019 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Misleading Failures of Partial-input Baselines

Recent work establishes dataset difficulty and removes annotation artifa...
05/14/2019 ∙ by Shi Feng, et al. ∙ 0 ∙ share

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Quizbowl: The Case for Incremental Question Answering

Quizbowl is a scholastic trivia competition that tests human knowledge a...
04/09/2019 ∙ by Pedro Rodriguez, et al. ∙ 0 ∙ share

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Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

Current methods to interpret deep learning models by generating saliency...
02/01/2019 ∙ by Sahil Singla, et al. ∙ 2 ∙ share

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What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play

Machine learning is an important tool for decision making, but its ethic...
10/23/2018 ∙ by Shi Feng, et al. ∙ 0 ∙ share

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Interpreting Neural Networks With Nearest Neighbors

Local model interpretation methods explain individual predictions by ass...
09/08/2018 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions

Modern natural language processing systems have been touted as approachi...
09/07/2018 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Right Answer for the Wrong Reason: Discovery and Mitigation

Exposing the weaknesses of neural models is crucial for improving their ...
04/20/2018 ∙ by Shi Feng, et al. ∙ 0 ∙ share

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The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task

We describe the University of Maryland machine translation systems submi...
08/03/2017 ∙ by Amr Sharaf, et al. ∙ 0 ∙ share

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Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model

Neural machine translation has shown very promising results lately. Most...
01/13/2016 ∙ by Shi Feng, et al. ∙ 0 ∙ share

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Shi Feng

Featured Co-authors

Ming Zhou

Dan Klein

Sameer Singh

Hal Daumé III

Matt Gardner

Soheil Feizi

Mu Li

Jordan Boyd-Graber

Mohit Iyyer

Shujie Liu

Sahil Singla

Calibrate Before Use: Improving Few-Shot Performance of Language Models

A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training

Customizing Triggers with Concealed Data Poisoning

Universal Adversarial Triggers for Attacking and Analyzing NLP

Universal Adversarial Triggers for NLP

Misleading Failures of Partial-input Baselines

Quizbowl: The Case for Incremental Question Answering

Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation

What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play

Interpreting Neural Networks With Nearest Neighbors

Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions

Right Answer for the Wrong Reason: Discovery and Mitigation

The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task

Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model