1 citations · 3 across the 9 of their papers we have counts for
9 papers
Knowledge-Aware Conversation Derailment Forecasting Using Graph Convolutional Networks
Enas Altarawneh, Ameeta Agrawal, Michael Jenkin +1
Online conversations are particularly susceptible to derailment, which can manifest itself in the form of toxic communication patterns including disrespectful comments and abuse. F…
Broadening Access to Simulations for End-Users via Large Language Models: Challenges and Opportunities
Philippe J. Giabbanelli, Jose J. Padilla, Ameeta Agrawal
Large Language Models (LLMs) are becoming ubiquitous to create intelligent virtual assistants that assist users in interacting with a system, as exemplified in marketing. Although…
ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness
Yufei Tao, Ameeta Agrawal, Judit Dombi +2
Recent advances in interactive large language models like ChatGPT have revolutionized various domains; however, their behavior in natural and role-play conversation settings remain…
Narrating Causal Graphs with Large Language Models
Atharva Phatak, Vijay K. Mago, Ameeta Agrawal +2
The use of generative AI to create text descriptions from graphs has mostly focused on knowledge graphs, which connect concepts using facts. In this work we explore the capability…
Making a Long Story Short in Conversation Modeling
Yufei Tao, Tiernan Mines, Ameeta Agrawal
Conversation systems accommodate diverse users with unique personalities and distinct writing styles. Within the domain of multi-turn dialogue modeling, this work studies the impac…
Predicting Evoked Emotions in Conversations
Enas Altarawneh, Ameeta Agrawal, Michael Jenkin +1
Understanding and predicting the emotional trajectory in multi-party multi-turn conversations is of great significance. Such information can be used, for example, to generate empat…