4 citations · 4 across the 10 of their papers we have counts for
7 papers · 1 filter
FLEx: Language Modeling with Few-shot Language Explanations
Adar Avsian, Christopher Richardson, Anirudh Sundar +1
Language models have become effective at a wide range of tasks, from math problem solving to open-domain question answering. However, they still make mistakes, and these mistakes a…
Schema Augmentation for Zero-Shot Domain Adaptation in Dialogue State Tracking
Christopher Richardson, Roshan Sharma, Neeraj Gaur +3
Zero-shot domain adaptation for dialogue state tracking (DST) remains a challenging problem in task-oriented dialogue (TOD) systems, where models must generalize to target domains…
cPAPERS: A Dataset of Situated and Multimodal Interactive Conversations in Scientific Papers
Anirudh Sundar, Jin Xu, William Gay +2
An emerging area of research in situated and multimodal interactive conversations (SIMMC) includes interactions in scientific papers. Since scientific papers are primarily composed…
iTBLS: A Dataset of Interactive Conversations Over Tabular Information
Anirudh Sundar, Christopher Richardson, Adar Avsian +1
This paper introduces Interactive Tables (iTBLS), a dataset of interactive conversations that focuses on natural-language manipulation of tabular information sourced from academic…
gTBLS: Generating Tables from Text by Conditional Question Answering
Anirudh Sundar, Christopher Richardson, Larry Heck
Distilling large, unstructured text into a structured, condensed form such as tables is an open research problem. One of the primary challenges in automatically generating tables i…
SYNDICOM: Improving Conversational Commonsense with Error-Injection and Natural Language Feedback
Christopher Richardson, Anirudh Sundar, Larry Heck
Commonsense reasoning is a critical aspect of human communication. Despite recent advances in conversational AI driven by large language models, commonsense reasoning remains a cha…
cTBLS: Augmenting Large Language Models with Conversational Tables
Anirudh S Sundar, Larry Heck
Optimizing accuracy and performance while eliminating hallucinations of open-domain conversational large language models (LLMs) is an open research challenge. A particularly promis…