no code implementations • 14 May 2024 • Jason Mars, Yiping Kang, Jayanaka Dantanarayana, Chandra Irugalbandara, Kugesan Sivasothynathan, Lingjia Tang
This is because it is currently unclear as to the right abstractions through which we should marry GenAI models with the nature of traditional programming code constructs.
no code implementations • 13 Jan 2024 • Christopher Clarke, Karthik Krishnamurthy, Walter Talamonti, Yiping Kang, Lingjia Tang, Jason Mars
Conversational agents have been gaining increasing popularity in recent years.
1 code implementation • 20 Dec 2023 • Chandra Irugalbandara, Ashish Mahendra, Roland Daynauth, Tharuka Kasthuri Arachchige, Jayanaka Dantanarayana, Krisztian Flautner, Lingjia Tang, Yiping Kang, Jason Mars
Many companies use large language models (LLMs) offered as a service, like OpenAI's GPT-4, to create AI-enabled product experiences.
1 code implementation • 25 May 2023 • Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner, Lingjia Tang, Jason Mars
In addition, depending on the aspect (sentiment, topic, etc.)
no code implementations • 17 May 2023 • Jason Mars, Yiping Kang, Roland Daynauth, Baichuan Li, Ashish Mahendra, Krisztian Flautner, Lingjia Tang
Today's production scale-out applications include many sub-application components, such as storage backends, logging infrastructure and AI models.
1 code implementation • Findings (ACL) 2022 • Christopher Clarke, Joseph Joshua Peper, Karthik Krishnamurthy, Walter Talamonti, Kevin Leach, Walter Lasecki, Yiping Kang, Lingjia Tang, Jason Mars
To address these problems, we introduce a new task BBAI: Black-Box Agent Integration, focusing on combining the capabilities of multiple black-box CAs at scale.
Ranked #1 on Multi-agent Integration on BBAI Dataset
Conversational Response Selection Multi-agent Integration +1
no code implementations • 13 Mar 2022 • Yiping Kang, Ashish Mahendra, Christopher Clarke, Lingjia Tang, Jason Mars
Personalized Intelligence (PI) is the problem of providing customized AI experiences tailored to each individual user.
5 code implementations • IJCNLP 2019 • Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, Jason Mars
We find that while the classifiers perform well on in-scope intent classification, they struggle to identify out-of-scope queries.
no code implementations • NAACL 2019 • Stefan Larson, Anish Mahendran, Andrew Lee, Jonathan K. Kummerfeld, Parker Hill, Michael A. Laurenzano, Johann Hauswald, Lingjia Tang, Jason Mars
We also present a novel data collection pipeline built atop our detection technique to automatically and iteratively mine unique data samples while discarding erroneous samples.
no code implementations • 7 Aug 2018 • Parker Hill, Babak Zamirai, Shengshuo Lu, Yu-Wei Chao, Michael Laurenzano, Mehrzad Samadi, Marios Papaefthymiou, Scott Mahlke, Thomas Wenisch, Jia Deng, Lingjia Tang, Jason Mars
With ever-increasing computational demand for deep learning, it is critical to investigate the implications of the numeric representation and precision of DNN model weights and activations on computational efficiency.
no code implementations • NAACL 2018 • Yiping Kang, Yunqi Zhang, Jonathan K. Kummerfeld, Lingjia Tang, Jason Mars
In this paper, we present a study of crowdsourcing methods for a user intent classification task in our deployed dialogue system.