no code implementations • 9 Feb 2024 • Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Amatriain, Jianfeng Gao
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022.
no code implementations • 24 Jan 2024 • Xavier Amatriain
Prompt design and engineering has rapidly become essential for maximizing the potential of large language models.
no code implementations • 6 Jun 2023 • Maksim Eremeev, Ilya Valmianski, Xavier Amatriain, Anitha Kannan
For high-stake domains that are also knowledge-rich, we show how to use knowledge to (a) identify which rare tokens that appear in both source and reference are important and (b) uplift their conditional probability.
no code implementations • 12 Feb 2023 • Xavier Amatriain, Ananth Sankar, Jie Bing, Praveen Kumar Bodigutla, Timothy J. Hazen, Michaeel Kazi
The goal of this paper is to offer a somewhat comprehensive but simple catalog and classification of the most popular Transformer models.
1 code implementation • 6 Oct 2022 • Mengqian Wang, Ilya Valmianski, Xavier Amatriain, Anitha Kannan
This paper presents an approach that tackles the problem of learning to classify medical dialogue into functional sections without requiring a large number of annotations.
1 code implementation • 12 Jul 2022 • Raymond Li, Ilya Valmianski, Li Deng, Xavier Amatriain, Anitha Kannan
In this paper, we propose a method for linking an open set of entities that does not require any span annotations.
1 code implementation • 17 Nov 2021 • Rhys Compton, Ilya Valmianski, Li Deng, Costa Huang, Namit Katariya, Xavier Amatriain, Anitha Kannan
We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module.
1 code implementation • 15 Nov 2021 • Varun Nair, Namit Katariya, Xavier Amatriain, Ilya Valmianski, Anitha Kannan
Summarized conversations are used to facilitate patient hand-offs between physicians, and as part of providing care in the future.
no code implementations • NAACL (NLPMC) 2021 • Bharath Chintagunta, Namit Katariya, Xavier Amatriain, Anitha Kannan
In medical dialogue summarization, summaries must be coherent and must capture all the medically relevant information in the dialogue.
no code implementations • 12 Nov 2020 • Ali Mottaghi, Prathusha K Sarma, Xavier Amatriain, Serena Yeung, Anitha Kannan
We study the problem of medical symptoms recognition from patient text, for the purposes of gathering pertinent information from the patient (known as history-taking).
no code implementations • Findings of the Association for Computational Linguistics 2020 • Anirudh Joshi, Namit Katariya, Xavier Amatriain, Anitha Kannan
Understanding a medical conversation between a patient and a physician poses unique natural language understanding challenge since it combines elements of standard open-ended conversation with very domain-specific elements that require expertise and medical knowledge.
no code implementations • 18 Sep 2020 • Anirudh Joshi, Namit Katariya, Xavier Amatriain, Anitha Kannan
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 conversation with very domain specific elements that require expertise and medical knowledge.
no code implementations • 7 Aug 2020 • Anitha Kannan, Richard Chen, Vignesh Venkataraman, Geoffrey J. Tso, Xavier Amatriain
Traditional symptom checkers, however, are based on manually curated expert systems that are inflexible and hard to modify, especially in a quickly changing situation like the one we are facing today.
no code implementations • 4 Aug 2020 • Clara H. McCreery, Namit Katariya, Anitha Kannan, Manish Chablani, Xavier Amatriain
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 people to answer them.
no code implementations • 11 Dec 2019 • Anitha Kannan, Jason Alan Fries, Eric Kramer, Jen Jen Chen, Nigam Shah, Xavier Amatriain
A third of adults in America use the Internet to diagnose medical concerns, and online symptom checkers are increasingly part of this process.
no code implementations • 16 Nov 2019 • Sam Shleifer, Manish Chablani, Anitha Kannan, Namit Katariya, Xavier Amatriain
Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred.
no code implementations • 9 Oct 2019 • Clara McCreery, Namit Katariya, Anitha Kannan, Manish Chablani, Xavier Amatriain
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.
no code implementations • 7 Oct 2019 • Viraj Prabhu, Anitha Kannan, Geoffrey J. Tso, Namit Katariya, Manish Chablani, David Sontag, Xavier Amatriain
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 have been trained.
no code implementations • 4 Oct 2019 • Sam Shleifer, Manish Chablani, Namit Katariya, Anitha Kannan, Xavier Amatriain
Only 12% of our discriminative approach's responses are worse than the doctor's response in the same conversational context, compared to 18% for the generative model.
no code implementations • 7 Nov 2018 • Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chablani, David Sontag, Xavier Amatriain
We consider the problem of image classification for the purpose of aiding doctors in dermatological diagnosis.
no code implementations • 21 Apr 2018 • Murali Ravuri, Anitha Kannan, Geoffrey J. Tso, Xavier Amatriain
In this paper, we present a method to merge both approaches by using expert systems as generative models that create simulated data on which models can be learned.