26 citations · 58 across the 13 of their papers we have counts for
13 papers
Humanity's Last Exam
Long Phan, Alice Gatti, Ziwen Han +1168
Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achi…
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…
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…
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…
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…
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…
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…
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…
"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…
Context-NER : Contextual Phrase Generation at Scale
Himanshu Gupta, Shreyas Verma, Santosh Mashetty +1
Named Entity Recognition (NER) has seen significant progress in recent years, with numerous state-of-the-art (SOTA) models achieving high performance. However, very few studies hav…
Prediction of Students performance with Artificial Neural Network using Demographic Traits
Adeniyi Jide Kehinde, Abidemi Emmanuel Adeniyi, Roseline Oluwaseun Ogundokun +2
Many researchers have studied student academic performance in supervised and unsupervised learning using numerous data mining techniques. Neural networks often need a greater colle…
An Empirical Study on Predictability of Software Code Smell Using Deep Learning Models
Himanshu Gupta, Tanmay G. Kulkarni, Lov Kumar +2
Code Smell, similar to a bad smell, is a surface indication of something tainted but in terms of software writing practices. This metric is an indication of a deeper problem lies w…
Empirical Analysis on Effectiveness of NLP Methods for Predicting Code Smell
Himanshu Gupta, Abhiram Anand Gulanikar, Lov Kumar +1
A code smell is a surface indicator of an inherent problem in the system, most often due to deviation from standard coding practices on the developers part during the development p…