b'Sameer Singh'

research

∙ 09/16/2023

EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learning

Large language models primarily rely on incontext learning to execute ta...

0 Rajasekhar Reddy Mekala, et al. ∙

research

∙ 08/01/2023

The Bias Amplification Paradox in Text-to-Image Generation

Bias amplification is a phenomenon in which models increase imbalances p...

0 Preethi Seshadri, et al. ∙

research

∙ 07/21/2023

Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors

Large language models (LLMs) are being applied as actors for sequential ...

0 Kolby Nottingham, et al. ∙

research

∙ 06/14/2023

Maestro: A Gamified Platform for Teaching AI Robustness

Although the prevention of AI vulnerabilities is critical to preserve th...

0 Margarita Geleta, et al. ∙

research

∙ 06/06/2023

MISGENDERED: Limits of Large Language Models in Understanding Pronouns

Content Warning: This paper contains examples of misgendering and erasur...

0 Tamanna Hossain, et al. ∙

research

∙ 05/24/2023

PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions

The remarkable capabilities of large language models have been accompani...

0 Anthony Chen, et al. ∙

research

∙ 05/24/2023

Coverage-based Example Selection for In-Context Learning

In-context learning (ICL), the ability of large language models to perfo...

0 Shivanshu Gupta, et al. ∙

research

∙ 04/25/2023

TABLET: Learning From Instructions For Tabular Data

Acquiring high-quality data is often a significant challenge in training...

0 Dylan Slack, et al. ∙

research

∙ 03/16/2023

ART: Automatic multi-step reasoning and tool-use for large language models

Large language models (LLMs) can perform complex reasoning in few- and z...

0 Bhargavi Paranjape, et al. ∙

research

∙ 12/20/2022

To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering

Recent advances in open-domain question answering (ODQA) have demonstrat...

0 Dheeru Dua, et al. ∙

research

∙ 12/08/2022

Successive Prompting for Decomposing Complex Questions

Answering complex questions that require making latent decisions is a ch...

0 Dheeru Dua, et al. ∙

research

∙ 10/21/2022

TCAB: A Large-Scale Text Classification Attack Benchmark

We introduce the Text Classification Attack Benchmark (TCAB), a dataset ...

0 Kalyani Asthana, et al. ∙

research

∙ 10/19/2022

Continued Pretraining for Better Zero- and Few-Shot Promptability

Recently introduced language model prompting methods can achieve high ac...

0 Zhaofeng Wu, et al. ∙

research

∙ 10/09/2022

Quantifying Social Biases Using Templates is Unreliable

Recently, there has been an increase in efforts to understand how large ...

0 Preethi Seshadri, et al. ∙

research

∙ 09/12/2022

Factual and Informative Review Generation for Explainable Recommendation

Recent models can generate fluent and grammatical synthetic reviews whil...

0 Zhouhang Xie, et al. ∙

research

∙ 07/08/2022

TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues

Machine Learning (ML) models are increasingly used to make critical deci...

0 Dylan Slack, et al. ∙

research

∙ 05/25/2022

Learning to Query Internet Text for Informing Reinforcement Learning Agents

Generalization to out of distribution tasks in reinforcement learning is...

0 Kolby Nottingham, et al. ∙

research

∙ 04/12/2022

ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension

Training a referring expression comprehension (ReC) model for a new visu...

4 Sanjay Subramanian, et al. ∙

research

∙ 03/16/2022

Structurally Diverse Sampling Reduces Spurious Correlations in Semantic Parsing Datasets

A rapidly growing body of research has demonstrated the inability of NLP...

5 Shivanshu Gupta, et al. ∙

research

∙ 02/15/2022

Impact of Pretraining Term Frequencies on Few-Shot Reasoning

Pretrained Language Models (LMs) have demonstrated ability to perform nu...

0 Yasaman Razeghi, et al. ∙

research

∙ 02/03/2022

Rethinking Explainability as a Dialogue: A Practitioner's Perspective

As practitioners increasingly deploy machine learning models in critical...

5 Himabindu Lakkaraju, et al. ∙

research

∙ 01/21/2022

Identifying Adversarial Attacks on Text Classifiers

The landscape of adversarial attacks against text classifiers continues ...

0 Zhouhang Xie, et al. ∙

research

∙ 12/16/2021

FRUIT: Faithfully Reflecting Updated Information in Text

Textual knowledge bases such as Wikipedia require considerable effort to...

0 Robert L. Logan IV, et al. ∙

research

∙ 09/10/2021

Entity-Based Knowledge Conflicts in Question Answering

Knowledge-dependent tasks typically use two sources of knowledge: parame...

10 Shayne Longpre, et al. ∙

research

∙ 09/05/2021

Modular Framework for Visuomotor Language Grounding

Natural language instruction following tasks serve as a valuable test-be...

14 Kolby Nottingham, et al. ∙

research

∙ 07/13/2021

Enforcing Consistency in Weakly Supervised Semantic Parsing

The predominant challenge in weakly supervised semantic parsing is that ...

0 Nitish Gupta, et al. ∙

research

∙ 07/01/2021

Combining Feature and Instance Attribution to Detect Artifacts

Training the large deep neural networks that dominate NLP requires large...

0 Pouya Pezeshkpour, et al. ∙

research

∙ 06/24/2021

Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models

Prompting language models (LMs) with training examples and task descript...

0 Robert L. Logan IV, et al. ∙

research

∙ 06/23/2021

Feature Attributions and Counterfactual Explanations Can Be Manipulated

As machine learning models are increasingly used in critical decision-ma...

0 Dylan Slack, et al. ∙

research

∙ 06/12/2021

Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP

Retrieval is a core component for open-domain NLP tasks. In open-domain ...

0 Anthony Chen, et al. ∙

research

∙ 06/04/2021

Counterfactual Explanations Can Be Manipulated

Counterfactual explanations are emerging as an attractive option for pro...

1 Dylan Slack, et al. ∙

research

∙ 04/18/2021

Generative Context Pair Selection for Multi-hop Question Answering

Compositional reasoning tasks like multi-hop question answering, require...

5 Dheeru Dua, et al. ∙

research

∙ 04/18/2021

Learning with Instance Bundles for Reading Comprehension

When training most modern reading comprehension models, all the question...

13 Dheeru Dua, et al. ∙

research

∙ 04/17/2021

Competency Problems: On Finding and Removing Artifacts in Language Data

Much recent work in NLP has documented dataset artifacts, bias, and spur...

0 Matt Gardner, et al. ∙

research

∙ 04/09/2021

An Empirical Comparison of Instance Attribution Methods for NLP

Widespread adoption of deep models has motivated a pressing need for app...

0 Pouya Pezeshkpour, et al. ∙

research

∙ 04/05/2021

Paired Examples as Indirect Supervision in Latent Decision Models

Compositional, structured models are appealing because they explicitly d...

0 Nitish Gupta, et al. ∙

research

∙ 02/19/2021

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

GPT-3 can perform numerous tasks when provided a natural language prompt...

6 Tony Z. Zhao, et al. ∙

research

∙ 10/29/2020

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts

The remarkable success of pretrained language models has motivated the s...

0 Taylor Shin, et al. ∙

research

∙ 10/23/2020

Customizing Triggers with Concealed Data Poisoning

Adversarial attacks alter NLP model predictions by perturbing test-time ...

0 Eric Wallace, et al. ∙

research

∙ 10/12/2020

MedICaT: A Dataset of Medical Images, Captions, and Textual References

Understanding the relationship between figures and text is key to scient...

18 Sanjay Subramanian, et al. ∙

research

∙ 10/12/2020

Gradient-based Analysis of NLP Models is Manipulable

Gradient-based analysis methods, such as saliency map visualizations and...

11 Junlin Wang, et al. ∙

research

∙ 10/07/2020

MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics

Posing reading comprehension as a generation problem provides a great de...

0 Anthony Chen, et al. ∙

research

∙ 08/11/2020

How Much Should I Trust You? Modeling Uncertainty of Black Box Explanations

As local explanations of black box models are increasingly being employe...

24 Dylan Slack, et al. ∙

research

∙ 06/04/2020

Image Augmentations for GAN Training

Data augmentations have been widely studied to improve the accuracy and ...

18 Zhengli Zhao, et al. ∙

research

∙ 05/08/2020

Beyond Accuracy: Behavioral Testing of NLP models with CheckList

Although measuring held-out accuracy has been the primary approach to ev...

0 Marco Tulio Ribeiro, et al. ∙

research

∙ 05/02/2020

Obtaining Faithful Interpretations from Compositional Neural Networks

Neural module networks (NMNs) are a popular approach for modeling compos...

22 Sanjay Subramanian, et al. ∙

research

∙ 04/06/2020

Evaluating NLP Models via Contrast Sets

Standard test sets for supervised learning evaluate in-distribution gene...

0 Matt Gardner, et al. ∙

research

∙ 02/11/2020

Improved Consistency Regularization for GANs

Recent work has increased the performance of Generative Adversarial Netw...

38 Zhengli Zhao, et al. ∙

research

∙ 12/29/2019

ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension

Reading comprehension is one of the crucial tasks for furthering researc...

0 Dheeru Dua, et al. ∙

research

∙ 12/23/2019

Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency

As deep reinforcement learning (RL) is applied to more tasks, there is a...

10 Piyush Gupta, et al. ∙

Sameer Singh

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