23 citations · 28 across the 5 of their papers we have counts for
5 papers
"Which LLM should I use?": Evaluating LLMs for tasks performed by Undergraduate Computer Science Students
Vibhor Agarwal, Madhav Krishan Garg, Sahiti Dharmavaram +1
This study evaluates the effectiveness of various large language models (LLMs) in performing tasks common among undergraduate computer science students. Although a number of resear…
GASCOM: Graph-based Attentive Semantic Context Modeling for Online Conversation Understanding
Vibhor Agarwal, Yu Chen, Nishanth Sastry
Online conversation understanding is an important yet challenging NLP problem which has many useful applications (e.g., hate speech detection). However, online conversations typica…
HateRephrase: Zero- and Few-Shot Reduction of Hate Intensity in Online Posts using Large Language Models
Vibhor Agarwal, Yu Chen, Nishanth Sastry
Hate speech has become pervasive in today's digital age. Although there has been considerable research to detect hate speech or generate counter speech to combat hateful views, the…
AI in the Gray: Exploring Moderation Policies in Dialogic Large Language Models vs. Human Answers in Controversial Topics
Vahid Ghafouri, Vibhor Agarwal, Yong Zhang +3
The introduction of ChatGPT and the subsequent improvement of Large Language Models (LLMs) have prompted more and more individuals to turn to the use of ChatBots, both for informat…
AnnoBERT: Effectively Representing Multiple Annotators' Label Choices to Improve Hate Speech Detection
Wenjie Yin, Vibhor Agarwal, Aiqi Jiang +2
Supervised approaches generally rely on majority-based labels. However, it is hard to achieve high agreement among annotators in subjective tasks such as hate speech detection. Exi…