165 citations · 420 across the 25 of their papers we have counts for
12 papers · 1 filter
Prompt2Model: Generating Deployable Models from Natural Language Instructions
Vijay Viswanathan, Chenyang Zhao, Amanda Bertsch +2
Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provi…
Large Language Models Enable Few-Shot Clustering
Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence +2
Unlike traditional unsupervised clustering, semi-supervised clustering allows users to provide meaningful structure to the data, which helps the clustering algorithm to match the u…
LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs
Tongshuang Wu, Haiyi Zhu, Maya Albayrak +21
LLMs have shown promise in replicating human-like behavior in crowdsourcing tasks that were previously thought to be exclusive to human abilities. However, current efforts focus ma…
Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation
Patrick Fernandes, Aman Madaan, Emmy Liu +8
Many recent advances in natural language generation have been fueled by training large language models on internet-scale data. However, this paradigm can lead to models that genera…
Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension
Ying Xu, Dakuo Wang, Mo Yu +15
Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity o…
Are Shortest Rationales the Best Explanations for Human Understanding?
Hua Shen, Tongshuang Wu, Wenbo Guo +1
Existing self-explaining models typically favor extracting the shortest possible rationales - snippets of an input text "responsible for" corresponding output - to explain the mode…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books
Bingsheng Yao, Dakuo Wang, Tongshuang Wu +4
Existing question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers often need to decide what questions th…
DeHumor: Visual Analytics for Decomposing Humor
Xingbo Wang, Yao Ming, Tongshuang Wu +3
Despite being a critical communication skill, grasping humor is challenging -- a successful use of humor requires a mixture of both engaging content build-up and an appropriate voc…
Tailor: Generating and Perturbing Text with Semantic Controls
Alexis Ross, Tongshuang Wu, Hao Peng +2
Controlled text perturbation is useful for evaluating and improving model generalizability. However, current techniques rely on training a model for every target perturbation, whic…
Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer +1
While counterfactual examples are useful for analysis and training of NLP models, current generation methods either rely on manual labor to create very few counterfactuals, or only…
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin +1
Although measuring held-out accuracy has been the primary approach to evaluate generalization, it often overestimates the performance of NLP models, while alternative approaches fo…