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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...
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08/01/2023
The Bias Amplification Paradox in Text-to-Image Generation
Bias amplification is a phenomenon in which models increase imbalances p...
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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 ...
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06/14/2023
Maestro: A Gamified Platform for Teaching AI Robustness
Although the prevention of AI vulnerabilities is critical to preserve th...
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06/06/2023
MISGENDERED: Limits of Large Language Models in Understanding Pronouns
Content Warning: This paper contains examples of misgendering and erasur...
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05/24/2023
PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions
The remarkable capabilities of large language models have been accompani...
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05/24/2023
Coverage-based Example Selection for In-Context Learning
In-context learning (ICL), the ability of large language models to perfo...
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04/25/2023
TABLET: Learning From Instructions For Tabular Data
Acquiring high-quality data is often a significant challenge in training...
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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...
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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...
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12/08/2022
Successive Prompting for Decomposing Complex Questions
Answering complex questions that require making latent decisions is a ch...
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10/21/2022
TCAB: A Large-Scale Text Classification Attack Benchmark
We introduce the Text Classification Attack Benchmark (TCAB), a dataset ...
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10/19/2022
Continued Pretraining for Better Zero- and Few-Shot Promptability
Recently introduced language model prompting methods can achieve high ac...
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10/09/2022
Quantifying Social Biases Using Templates is Unreliable
Recently, there has been an increase in efforts to understand how large ...
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09/12/2022
Factual and Informative Review Generation for Explainable Recommendation
Recent models can generate fluent and grammatical synthetic reviews whil...
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07/08/2022
TalkToModel: Understanding Machine Learning Models With Open Ended Dialogues
Machine Learning (ML) models are increasingly used to make critical deci...
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05/25/2022
Learning to Query Internet Text for Informing Reinforcement Learning Agents
Generalization to out of distribution tasks in reinforcement learning is...
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04/12/2022
ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension
Training a referring expression comprehension (ReC) model for a new visu...
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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...
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02/15/2022
Impact of Pretraining Term Frequencies on Few-Shot Reasoning
Pretrained Language Models (LMs) have demonstrated ability to perform nu...
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02/03/2022
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
As practitioners increasingly deploy machine learning models in critical...
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01/21/2022
Identifying Adversarial Attacks on Text Classifiers
The landscape of adversarial attacks against text classifiers continues ...
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12/16/2021
FRUIT: Faithfully Reflecting Updated Information in Text
Textual knowledge bases such as Wikipedia require considerable effort to...
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09/10/2021
Entity-Based Knowledge Conflicts in Question Answering
Knowledge-dependent tasks typically use two sources of knowledge: parame...
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09/05/2021
Modular Framework for Visuomotor Language Grounding
Natural language instruction following tasks serve as a valuable test-be...
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07/13/2021
Enforcing Consistency in Weakly Supervised Semantic Parsing
The predominant challenge in weakly supervised semantic parsing is that ...
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07/01/2021
Combining Feature and Instance Attribution to Detect Artifacts
Training the large deep neural networks that dominate NLP requires large...
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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...
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06/23/2021
Feature Attributions and Counterfactual Explanations Can Be Manipulated
As machine learning models are increasingly used in critical decision-ma...
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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 ...
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06/04/2021
Counterfactual Explanations Can Be Manipulated
Counterfactual explanations are emerging as an attractive option for pro...
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04/18/2021
Generative Context Pair Selection for Multi-hop Question Answering
Compositional reasoning tasks like multi-hop question answering, require...
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04/18/2021
Learning with Instance Bundles for Reading Comprehension
When training most modern reading comprehension models, all the question...
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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...
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04/09/2021
An Empirical Comparison of Instance Attribution Methods for NLP
Widespread adoption of deep models has motivated a pressing need for app...
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04/05/2021
Paired Examples as Indirect Supervision in Latent Decision Models
Compositional, structured models are appealing because they explicitly d...
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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...
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10/29/2020
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
The remarkable success of pretrained language models has motivated the s...
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10/23/2020
Customizing Triggers with Concealed Data Poisoning
Adversarial attacks alter NLP model predictions by perturbing test-time ...
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10/12/2020
MedICaT: A Dataset of Medical Images, Captions, and Textual References
Understanding the relationship between figures and text is key to scient...
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10/12/2020
Gradient-based Analysis of NLP Models is Manipulable
Gradient-based analysis methods, such as saliency map visualizations and...
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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...
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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...
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06/04/2020
Image Augmentations for GAN Training
Data augmentations have been widely studied to improve the accuracy and ...
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05/08/2020
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
Although measuring held-out accuracy has been the primary approach to ev...
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05/02/2020
Obtaining Faithful Interpretations from Compositional Neural Networks
Neural module networks (NMNs) are a popular approach for modeling compos...
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04/06/2020
Evaluating NLP Models via Contrast Sets
Standard test sets for supervised learning evaluate in-distribution gene...
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02/11/2020
Improved Consistency Regularization for GANs
Recent work has increased the performance of Generative Adversarial Netw...
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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...
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12/23/2019