93 citations · 266 across the 18 of their papers we have counts for
19 papers
FLamE: Few-shot Learning from Natural Language Explanations
Yangqiaoyu Zhou, Yiming Zhang, Chenhao Tan
Natural language explanations have the potential to provide rich information that in principle guides model reasoning. Yet, recent work by Lampinen et al. (2022) has shown limited…
Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
Xiaoming Shi, Siqiao Xue, Kangrui Wang +5
Large language models have shown astonishing performance on a wide range of reasoning tasks. In this paper, we investigate whether they could reason about real-world events and hel…
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
Emre Kıcıman, Robert Ness, Amit Sharma +1
The causal capabilities of large language models (LLMs) are a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as m…
Understanding and Predicting Human Label Variation in Natural Language Inference through Explanation
Nan-Jiang Jiang, Chenhao Tan, Marie-Catherine de Marneffe
Human label variation (Plank 2022), or annotation disagreement, exists in many natural language processing (NLP) tasks. To be robust and trusted, NLP models need to identify such v…
Learning Human-Compatible Representations for Case-Based Decision Support
Han Liu, Yizhou Tian, Chacha Chen +3
Algorithmic case-based decision support provides examples to help human make sense of predicted labels and aid human in decision-making tasks. Despite the promising performance of…
Selective Explanations: Leveraging Human Input to Align Explainable AI
Vivian Lai, Yiming Zhang, Chacha Chen +2
While a vast collection of explainable AI (XAI) algorithms have been developed in recent years, they are often criticized for significant gaps with how humans produce and consume e…
1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results
Benjamin Kiefer, Matej Kristan, Janez Perš +70
The 1 Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and…
Active Example Selection for In-Context Learning
Yiming Zhang, Shi Feng, Chenhao Tan
With a handful of demonstration examples, large-scale language models show strong capability to perform various tasks by in-context learning from these examples, without any fine-t…
Learning to Ignore Adversarial Attacks
Yiming Zhang, Yangqiaoyu Zhou, Samuel Carton +1
Despite the strong performance of current NLP models, they can be brittle against adversarial attacks. To enable effective learning against adversarial inputs, we introduce the use…
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
Himabindu Lakkaraju, Dylan Slack, Yuxin Chen +2
As practitioners increasingly deploy machine learning models in critical domains such as health care, finance, and policy, it becomes vital to ensure that domain experts function e…
Machine Explanations and Human Understanding
Chacha Chen, Shi Feng, Amit Sharma +1
Explanations are hypothesized to improve human understanding of machine learning models and achieve a variety of desirable outcomes, ranging from model debugging to enhancing human…
Towards a Science of Human-AI Decision Making: A Survey of Empirical Studies
Vivian Lai, Chacha Chen, Q. Vera Liao +2
As AI systems demonstrate increasingly strong predictive performance, their adoption has grown in numerous domains. However, in high-stakes domains such as criminal justice and hea…
What to Learn, and How: Toward Effective Learning from Rationales
Samuel Carton, Surya Kanoria, Chenhao Tan
Learning from rationales seeks to augment model prediction accuracy using human-annotated rationales (i.e. subsets of input tokens) that justify their chosen labels, often in the f…
Investigating the Effect of Natural Language Explanations on Out-of-Distribution Generalization in Few-shot NLI
Yangqiaoyu Zhou, Chenhao Tan
Although neural models have shown strong performance in datasets such as SNLI, they lack the ability to generalize out-of-distribution (OOD). In this work, we formulate a few-shot…
Decision-Focused Summarization
Chao-Chun Hsu, Chenhao Tan
Relevance in summarization is typically defined based on textual information alone, without incorporating insights about a particular decision. As a result, to support risk analysi…
On the Diversity and Limits of Human Explanations
Chenhao Tan
A growing effort in NLP aims to build datasets of human explanations. However, the term explanation encompasses a broad range of notions, each with different properties and ramific…
Using Transformers to Provide Teachers with Personalized Feedback on their Classroom Discourse: The TalkMoves Application
Abhijit Suresh, Jennifer Jacobs, Vivian Lai +4
TalkMoves is an innovative application designed to support K-12 mathematics teachers to reflect on, and continuously improve their instructional practices. This application combine…
A Quantitative Portrait of Wikipedia's High-Tempo Collaborations during the 2020 Coronavirus Pandemic
Brian C. Keegan, Chenhao Tan
The 2020 coronavirus pandemic was a historic social disruption with significant consequences felt around the globe. Wikipedia is a freely-available, peer-produced encyclopedia with…
Understanding the Diverging User Trajectories in Highly-related Online Communities during the COVID-19 Pandemic
Jason Shuo Zhang, Brian C. Keegan, Qin Lv +1
As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two "new" communities, /r/China flu and /r/Coronavirus, emerged on…