3 citations · 5 across the 8 of their papers we have counts for
8 papers
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…
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…
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…
MDDial: A Multi-turn Differential Diagnosis Dialogue Dataset with Reliability Evaluation
Srija Macherla, Man Luo, Mihir Parmar +1
Dialogue systems for Automatic Differential Diagnosis (ADD) have a wide range of real-life applications. These dialogue systems are promising for providing easy access and reducing…
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…
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…