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20212025
most citedAn Empirical Study on Predictability of Software Code Smell Using Deep Learning Models

26 citations · 58 across the 13 of their papers we have counts for

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8 papers · 1 filter

cs.CL2024

Cutting Through the Noise: Boosting LLM Performance on Math Word Problems

Ujjwala Anantheswaran, Himanshu Gupta, Kevin Scaria +3

Large Language Models (LLMs) excel at various tasks, including solving math word problems (MWPs), but struggle with real-world problems containing irrelevant information. To addres…

cs.CL2023

LongBoX: Evaluating Transformers on Long-Sequence Clinical Tasks

Mihir Parmar, Aakanksha Naik, Himanshu Gupta +2

Many large language models (LLMs) for medicine have largely been evaluated on short texts, and their ability to handle longer sequences such as a complete electronic health record…

cs.CL2023★ 3 cited

TarGEN: Targeted Data Generation with Large Language Models

Himanshu Gupta, Kevin Scaria, Ujjwala Anantheswaran +5

The rapid advancement of large language models (LLMs) has sparked interest in data synthesis techniques, aiming to generate diverse and high-quality synthetic datasets. However, th…

cs.CL2023

EDM3: Event Detection as Multi-task Text Generation

Ujjwala Anantheswaran, Himanshu Gupta, Mihir Parmar +2

Event detection refers to identifying event occurrences in a text and comprises of two subtasks; event identification and classification. We present EDM3, a novel approach for Even…

cs.CL2023★ 3 cited

Instruction Tuned Models are Quick Learners

Himanshu Gupta, Saurabh Arjun Sawant, Swaroop Mishra +4

Instruction tuning of language models has demonstrated the ability to enhance model generalization to unseen tasks via in-context learning using a few examples. However, typical su…

cs.CL2023

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…

cs.CL2023★ 3 cited

InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis

Kevin Scaria, Himanshu Gupta, Siddharth Goyal +3

We introduce InstructABSA, an instruction learning paradigm for Aspect-Based Sentiment Analysis (ABSA) subtasks. Our method introduces positive, negative, and neutral examples to e…

cs.CL2022

"John is 50 years old, can his son be 65?" Evaluating NLP Models' Understanding of Feasibility

Himanshu Gupta, Neeraj Varshney, Swaroop Mishra +5

In current NLP research, large-scale language models and their abilities are widely being discussed. Some recent works have also found notable failures of these models. Often these…