47 citations · 123 across the 31 of their papers we have counts for
9 papers · 1 filter
The Art of Defending: A Systematic Evaluation and Analysis of LLM Defense Strategies on Safety and Over-Defensiveness
Neeraj Varshney, Pavel Dolin, Agastya Seth +1
As Large Language Models (LLMs) play an increasingly pivotal role in natural language processing applications, their safety concerns become critical areas of NLP research. This pap…
Accelerating LLaMA Inference by Enabling Intermediate Layer Decoding via Instruction Tuning with LITE
Neeraj Varshney, Agneet Chatterjee, Mihir Parmar +1
Large Language Models (LLMs) have achieved remarkable performance across a wide variety of natural language tasks; however, their large size makes their inference slow and computat…
Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models
Man Luo, Shrinidhi Kumbhar, Ming shen +5
Logical reasoning is fundamental for humans yet presents a substantial challenge in the domain of Artificial Intelligence. Initially, researchers used Knowledge Representation and…
Can NLP Models 'Identify', 'Distinguish', and 'Justify' Questions that Don't have a Definitive Answer?
Ayushi Agarwal, Nisarg Patel, Neeraj Varshney +7
Though state-of-the-art (SOTA) NLP systems have achieved remarkable performance on a variety of language understanding tasks, they primarily focus on questions that have a correct…
A Stitch in Time Saves Nine: Detecting and Mitigating Hallucinations of LLMs by Validating Low-Confidence Generation
Neeraj Varshney, Wenlin Yao, Hongming Zhang +2
Recently developed large language models have achieved remarkable success in generating fluent and coherent text. However, these models often tend to 'hallucinate' which critically…
Can NLP Models Correctly Reason Over Contexts that Break the Common Assumptions?
Neeraj Varshney, Mihir Parmar, Nisarg Patel +4
Pre-training on large corpora of text enables the language models to acquire a vast amount of factual and commonsense knowledge which allows them to achieve remarkable performance…
A Unified Evaluation Framework for Novelty Detection and Accommodation in NLP with an Instantiation in Authorship Attribution
Neeraj Varshney, Himanshu Gupta, Eric Robertson +2
State-of-the-art natural language processing models have been shown to achieve remarkable performance in 'closed-world' settings where all the labels in the evaluation set are know…
Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA
Neeraj Varshney, Chitta Baral
Despite remarkable progress made in natural language processing, even the state-of-the-art models often make incorrect predictions. Such predictions hamper the reliability of syste…
Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments
Tung Thai, Ming Shen, Mayank Garg +10
Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to be able to guarantee satisfactory task perf…