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06/13/2023
FLamE: Few-shot Learning from Natural Language Explanations
Natural language explanations have the potential to provide rich informa...
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06/12/2023
Language of Bargaining
Leveraging an established exercise in negotiation education, we build a ...
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05/03/2023
Characterizing Political Bias in Automatic Summaries: A Case Study of Trump and Biden
Growing literature has shown that powerful NLP systems may encode social...
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04/28/2023
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
The causal capabilities of large language models (LLMs) is a matter of s...
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04/24/2023
Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation
Human label variation (Plank 2022), or annotation disagreement, exists i...
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01/23/2023
Selective Explanations: Leveraging Human Input to Align Explainable AI
While a vast collection of explainable AI (XAI) algorithms have been dev...
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11/08/2022
Active Example Selection for In-Context Learning
With a handful of demonstration examples, large-scale language models sh...
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07/08/2022
Probing Classifiers are Unreliable for Concept Removal and Detection
Neural network models trained on text data have been found to encode und...
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05/23/2022
Learning to Ignore Adversarial Attacks
Despite the strong performance of current NLP models, they can be brittl...
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04/25/2022
Human-AI Collaboration via Conditional Delegation: A Case Study of Content Moderation
Despite impressive performance in many benchmark datasets, AI models can...
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02/08/2022
Machine Explanations and Human Understanding
Explanations are hypothesized to improve human understanding of machine ...
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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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12/21/2021
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
As AI systems demonstrate increasingly strong predictive performance, th...
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11/30/2021
What to Learn, and How: Toward Effective Learning from Rationales
Learning from rationales seeks to augment model training with human-prov...
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10/12/2021
Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI
Although neural models have shown strong performance in datasets such as...
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06/22/2021
On Positivity Bias in Negative Reviews
Prior work has revealed that positive words occur more frequently than n...
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06/22/2021
On the Diversity and Limits of Human Explanations
A growing effort in NLP aims to build datasets of human explanations. Ho...
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04/29/2021
Using Transformers to Provide Teachers with Personalized Feedback on their Classroom Discourse: The TalkMoves Application
TalkMoves is an innovative application designed to support K-12 mathemat...
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01/13/2021
Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making
Although AI holds promise for improving human decision making in societa...
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11/10/2020
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End
To explain a machine learning model, there are two main approaches: feat...
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10/09/2020
Evaluating and Characterizing Human Rationales
Two main approaches for evaluating the quality of machine-generated rati...
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10/07/2020
Characterizing the Value of Information in Medical Notes
Machine learning models depend on the quality of input data. As electron...
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06/16/2020
A Quantitative Portrait of Wikipedia's High-Tempo Collaborations during the 2020 Coronavirus Pandemic
The 2020 coronavirus pandemic was a historic social disruption with sign...
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06/08/2020
A Tale of Two Communities: Characterizing Reddit Response to COVID-19 through /r/China_Flu and /r/Coronavirus
The COVID-19 pandemic has deeply impacted people's lives around the glob...
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03/16/2020
Harnessing Explanations to Bridge AI and Humans
Machine learning models are increasingly integrated into societally crit...
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01/14/2020
"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
To support human decision making with machine learning models, we often ...
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12/06/2019
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers
Explaining the output of a complex machine learning (ML) model often req...
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11/01/2019
What Gets Echoed? Understanding the "Pointers" in Explanations of Persuasive Arguments
Explanations are central to everyday life, and are a topic of growing in...
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10/21/2019
Content Removal as a Moderation Strategy: Compliance and Other Outcomes in the ChangeMyView Community
Moderators of online communities often employ comment deletion as a tool...
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10/18/2019
Many Faces of Feature Importance: Comparing Built-in and Post-hoc Feature Importance in Text Classification
Feature importance is commonly used to explain machine predictions. Whil...
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08/28/2019
Intergroup Contact in the Wild: Characterizing Language Differences between Intergroup and Single-group Members in NBA-related Discussion Forums
Intergroup contact has long been considered as an effective strategy to ...
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06/24/2019
Identify and understand pay-it-forward reciprocity using millions of online red packets
Pay-it-forward reciprocity encourages the spread of prosocial behavior. ...
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05/19/2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
Post-hoc explanations of machine learning models are crucial for people ...
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04/30/2019
Learning Fair Representations via an Adversarial Framework
Fairness has become a central issue for our research community as classi...
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04/18/2019
No Permanent Friends or Enemies: Tracking Relationships between Nations from News
Understanding the dynamics of international politics is important yet ch...
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03/18/2019
Are All Successful Communities Alike? Characterizing and Predicting the Success of Online Communities
The proliferation of online communities has created exciting opportuniti...
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02/18/2019
What Makes a Good Team? A Large-scale Study on the Effect of Team Composition in Honor of Kings
Team composition is a central factor in determining the effectiveness of...
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02/08/2019
Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability
Although artificial intelligence holds promise for addressing societal c...
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11/19/2018
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection
Humans are the final decision makers in critical tasks that involve ethi...
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05/29/2018
LSTMs Exploit Linguistic Attributes of Data
While recurrent neural networks have found success in a variety of natur...
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02/23/2018
"You are no Jack Kennedy": On Media Selection of Highlights from Presidential Debates
Political speeches and debates play an important role in shaping the ima...
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12/08/2017
Online Red Packets: A Large-scale Empirical Study of Gift Giving on WeChat
Gift giving is a ubiquitous social phenomenon, and red packets have been...
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08/02/2017
Dynamic Entity Representations in Neural Language Models
Understanding a long document requires tracking how entities are introdu...
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05/25/2017
A Neural Framework for Generalized Topic Models
Topic models for text corpora comprise a popular family of methods that ...
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04/25/2017
Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts
Understanding how ideas relate to each other is a fundamental question i...
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12/19/2016
Talk it up or play it down? (Un)expected correlations between (de-)emphasis and recurrence of discussion points in consequential U.S. economic policy meetings
In meetings where important decisions get made, what items receive more ...
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02/02/2016
Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions
Changing someone's opinion is arguably one of the most important challen...
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03/04/2015
