12 citations · 33 across the 9 of their papers we have counts for
8 papers · 1 filter
Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?
Neeraj Varshney, Man Luo, Chitta Baral
Recent state-of-the-art open-domain QA models are typically based on a two stage retriever-reader approach in which the retriever first finds the relevant knowledge/passages and th…
Model Cascading: Towards Jointly Improving Efficiency and Accuracy of NLP Systems
Neeraj Varshney, Chitta Baral
Do all instances need inference through the big models for a correct prediction? Perhaps not; some instances are easy and can be answered correctly by even small capacity models. T…
NumGLUE: A Suite of Fundamental yet Challenging Mathematical Reasoning Tasks
Swaroop Mishra, Arindam Mitra, Neeraj Varshney +4
Given the ubiquitous nature of numbers in text, reasoning with numbers to perform simple calculations is an important skill of AI systems. While many datasets and models have been…
ILDAE: Instance-Level Difficulty Analysis of Evaluation Data
Neeraj Varshney, Swaroop Mishra, Chitta Baral
Knowledge of questions' difficulty level helps a teacher in several ways, such as estimating students' potential quickly by asking carefully selected questions and improving qualit…
Investigating Selective Prediction Approaches Across Several Tasks in IID, OOD, and Adversarial Settings
Neeraj Varshney, Swaroop Mishra, Chitta Baral
In order to equip NLP systems with selective prediction capability, several task-specific approaches have been proposed. However, which approaches work best across tasks or even if…
Interviewer-Candidate Role Play: Towards Developing Real-World NLP Systems
Neeraj Varshney, Swaroop Mishra, Chitta Baral
Standard NLP tasks do not incorporate several common real-world scenarios such as seeking clarifications about the question, taking advantage of clues, abstaining in order to avoid…