10 citations · 20 across the 10 of their papers we have counts for
10 papers
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Adding more data does not always help: A study in medical conversation summarization with PEGASUS
Varun Nair, Namit Katariya, Xavier Amatriain +2
Medical conversation summarization is integral in capturing information gathered during interactions between patients and physicians. Summarized conversations are used to facilitat…
MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System
Rhys Compton, Ilya Valmianski, Li Deng +4
We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed…
Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization
Bharath Chintagunta, Namit Katariya, Xavier Amatriain +1
In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue. However, learning effective models for summar…
Dr. Summarize: Global Summarization of Medical Dialogue by Exploiting Local Structures
Anirudh Joshi, Namit Katariya, Xavier Amatriain +1
Understanding a medical conversation between a patient and a physician poses a unique natural language understanding challenge since it combines elements of standard open ended con…
Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs
Clara H. McCreery, Namit Katariya, Anitha Kannan +2
People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified pe…
Classification as Decoder: Trading Flexibility for Control in Medical Dialogue
Sam Shleifer, Manish Chablani, Anitha Kannan +2
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a…
Domain-Relevant Embeddings for Medical Question Similarity
Clara McCreery, Namit Katariya, Anitha Kannan +2
The rate at which medical questions are asked online far exceeds the capacity of qualified people to answer them, and many of these questions are not unique. Identifying same-quest…
Classification As Decoder: Trading Flexibility For Control In Neural Dialogue
Sam Shleifer, Manish Chablani, Namit Katariya +2
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a…
Open Set Medical Diagnosis
Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso +4
Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they ha…