26 citations · 58 across the 13 of their papers we have counts for
6 papers · 1 filter
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…