activity
20182026
most citedNews Summarization and Evaluation in the Era of GPT-3

182 citations · 233 across the 36 of their papers we have counts for

collaborators
Showing 2023Show all

8 papers · 1 filter

cs.CL2023★ 3 cited

Sarcasm Detection in a Disaster Context

Tiberiu Sosea, Junyi Jessy Li, Cornelia Caragea

During natural disasters, people often use social media platforms such as Twitter to ask for help, to provide information about the disaster situation, or to express contempt about…

cs.SE2023

Multilingual Code Co-Evolution Using Large Language Models

Jiyang Zhang, Pengyu Nie, Junyi Jessy Li +1

Many software projects implement APIs and algorithms in multiple programming languages. Maintaining such projects is tiresome, as developers have to ensure that any change (e.g., a…

cs.CL2023

Unsupervised Extractive Summarization of Emotion Triggers

Tiberiu Sosea, Hongli Zhan, Junyi Jessy Li +1

Understanding what leads to emotions during large-scale crises is important as it can provide groundings for expressed emotions and subsequently improve the understanding of ongoin…

cs.CL2023★ 4 cited

Summarizing, Simplifying, and Synthesizing Medical Evidence Using GPT-3 (with Varying Success)

Chantal Shaib, Millicent L. Li, Sebastian Joseph +3

Large language models, particularly GPT-3, are able to produce high quality summaries of general domain news articles in few- and zero-shot settings. However, it is unclear if such…

cs.CL2023★ 4 cited

Using Natural Language Explanations to Rescale Human Judgments

Manya Wadhwa, Jifan Chen, Junyi Jessy Li +1

The rise of large language models (LLMs) has brought a critical need for high-quality human-labeled data, particularly for processes like human feedback and evaluation. A common pr…

cs.CL2023★ 1 cited

Multilingual Simplification of Medical Texts

Sebastian Joseph, Kathryn Kazanas, Keziah Reina +4

Automated text simplification aims to produce simple versions of complex texts. This task is especially useful in the medical domain, where the latest medical findings are typicall…

cs.CL2023

Elaborative Simplification as Implicit Questions Under Discussion

Yating Wu, William Sheffield, Kyle Mahowald +1

Automated text simplification, a technique useful for making text more accessible to people such as children and emergent bilinguals, is often thought of as a monolingual translati…

cs.SE2023★ 1 cited

Learning Deep Semantics for Test Completion

Pengyu Nie, Rahul Banerjee, Junyi Jessy Li +2

Writing tests is a time-consuming yet essential task during software development. We propose to leverage recent advances in deep learning for text and code generation to assist dev…