44 citations · 63 across the 11 of their papers we have counts for
11 papers
Human-AI Collaborative Taxonomy Construction: A Case Study in Profession-Specific Writing Assistants
Minhwa Lee, Zae Myung Kim, Vivek Khetan +1
Large Language Models (LLMs) have assisted humans in several writing tasks, including text revision and story generation. However, their effectiveness in supporting domain-specific…
DEFT: Data Efficient Fine-Tuning for Pre-Trained Language Models via Unsupervised Core-Set Selection
Devleena Das, Vivek Khetan
Recent advances have led to the availability of many pre-trained language models (PLMs); however, a question that remains is how much data is truly needed to fine-tune PLMs for dow…
RedHOT: A Corpus of Annotated Medical Questions, Experiences, and Claims on Social Media
Somin Wadhwa, Vivek Khetan, Silvio Amir +1
We present Reddit Health Online Talk (RedHOT), a corpus of 22,000 richly annotated social media posts from Reddit spanning 24 health conditions. Annotations include demarcations of…
CHARD: Clinical Health-Aware Reasoning Across Dimensions for Text Generation Models
Steven Y. Feng, Vivek Khetan, Bogdan Sacaleanu +2
We motivate and introduce CHARD: Clinical Health-Aware Reasoning across Dimensions, to investigate the capability of text generation models to act as implicit clinical knowledge ba…
Identifying causal relations in tweets using deep learning: Use case on diabetes-related tweets from 2017-2021
Adrian Ahne, Vivek Khetan, Xavier Tannier +6
Objective: Leveraging machine learning methods, we aim to extract both explicit and implicit cause-effect associations in patient-reported, diabetes-related tweets and provide a to…
Template Filling for Controllable Commonsense Reasoning
Dheeraj Rajagopal, Vivek Khetan, Bogdan Sacaleanu +3
Large-scale sequence-to-sequence models have shown to be adept at both multiple-choice and open-domain commonsense reasoning tasks. However, the current systems do not provide the…
MIMICause: Representation and automatic extraction of causal relation types from clinical notes
Vivek Khetan, Md Imbesat Hassan Rizvi, Jessica Huber +3
Understanding causal narratives communicated in clinical notes can help make strides towards personalized healthcare. Extracted causal information from clinical notes can be combin…
Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights
Vivek Khetan, Annervaz K M, Erin Wetherley +3
The growing quantity and complexity of data pose challenges for humans to consume information and respond in a timely manner. For businesses in domains with rapidly changing rules…
Causal BERT : Language models for causality detection between events expressed in text
Vivek Khetan, Roshni Ramnani, Mayuresh Anand +2
Causality understanding between events is a critical natural language processing task that is helpful in many areas, including health care, business risk management and finance. On…
Correlation and Prediction of Evaluation Metrics in Information Retrieval
Mucahid Kutlu, Vivek Khetan, Matthew Lease
Because researchers typically do not have the time or space to present more than a few evaluation metrics in any published study, it can be difficult to assess relative effectivene…
Neural Information Retrieval: A Literature Review
Ye Zhang, Md Mustafizur Rahman, Alex Braylan +12
A recent "third wave" of Neural Network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and…