Zobrazeno 1 - 10
of 160
pro vyhledávání: '"Bhargava, Shruti"'
Autor:
Kulkarni, Atharva, Tseng, Bo-Hsiang, Moniz, Joel Ruben Antony, Piraviperumal, Dhivya, Yu, Hong, Bhargava, Shruti
In-context learning with Large Language Models (LLMs) has emerged as a promising avenue of research in Dialog State Tracking (DST). However, the best-performing in-context learning methods involve retrieving and adding similar examples to the prompt,
Externí odkaz:
http://arxiv.org/abs/2402.02285
Autor:
Zhu, Yilun, Moniz, Joel Ruben Antony, Bhargava, Shruti, Lu, Jiarui, Piraviperumal, Dhivya, Li, Site, Zhang, Yuan, Yu, Hong, Tseng, Bo-Hsiang
Understanding context is key to understanding human language, an ability which Large Language Models (LLMs) have been increasingly seen to demonstrate to an impressive extent. However, though the evaluation of LLMs encompasses various domains within
Externí odkaz:
http://arxiv.org/abs/2402.00858
Autor:
Ates, Halim Cagri, Bhargava, Shruti, Li, Site, Lu, Jiarui, Maddula, Siddhardha, Moniz, Joel Ruben Antony, Nalamalapu, Anil Kumar, Nguyen, Roman Hoang, Ozyildirim, Melis, Patel, Alkesh, Piraviperumal, Dhivya, Renkens, Vincent, Samal, Ankit, Tran, Thy, Tseng, Bo-Hsiang, Yu, Hong, Zhang, Yuan, Zou, Rong
Successfully handling context is essential for any dialog understanding task. This context maybe be conversational (relying on previous user queries or system responses), visual (relying on what the user sees, for example, on their screen), or backgr
Externí odkaz:
http://arxiv.org/abs/2311.01650
Autor:
Aas, Cecilia, Abdelsalam, Hisham, Belousova, Irina, Bhargava, Shruti, Cheng, Jianpeng, Daland, Robert, Driesen, Joris, Flego, Federico, Guigue, Tristan, Johannsen, Anders, Lal, Partha, Lu, Jiarui, Moniz, Joel Ruben Antony, Perkins, Nathan, Piraviperumal, Dhivya, Pulman, Stephen, Séaghdha, Diarmuid Ó, Sun, David Q., Torr, John, Del Vecchio, Marco, Wacker, Jay, Williams, Jason D., Yu, Hong
It has recently become feasible to run personal digital assistants on phones and other personal devices. In this paper we describe a design for a natural language understanding system that runs on device. In comparison to a server-based assistant, th
Externí odkaz:
http://arxiv.org/abs/2308.03905
Autor:
Bhargava, Shruti, Dhoot, Anand, Jonsson, Ing-Marie, Nguyen, Hoang Long, Patel, Alkesh, Yu, Hong, Renkens, Vincent
Voice assistants help users make phone calls, send messages, create events, navigate, and do a lot more. However, assistants have limited capacity to understand their users' context. In this work, we aim to take a step in this direction. Our work div
Externí odkaz:
http://arxiv.org/abs/2306.07298
Autor:
Aggarwal, Lavisha, Bhargava, Shruti
Our society is plagued by several biases, including racial biases, caste biases, and gender bias. As a matter of fact, several years ago, most of these notions were unheard of. These biases passed through generations along with amplification have lea
Externí odkaz:
http://arxiv.org/abs/2305.01888
Autor:
Tseng, Bo-Hsiang, Bhargava, Shruti, Lu, Jiarui, Moniz, Joel Ruben Antony, Piraviperumal, Dhivya, Li, Lin, Yu, Hong
Anaphora and ellipses are two common phenomena in dialogues. Without resolving referring expressions and information omission, dialogue systems may fail to generate consistent and coherent responses. Traditionally, anaphora is resolved by coreference
Externí odkaz:
http://arxiv.org/abs/2105.09914
Autor:
Cheng, Jianpeng, Agrawal, Devang, Alonso, Hector Martinez, Bhargava, Shruti, Driesen, Joris, Flego, Federico, Ghosh, Shaona, Kaplan, Dain, Kartsaklis, Dimitri, Li, Lin, Piraviperumal, Dhivya, Williams, Jason D, Yu, Hong, Seaghdha, Diarmuid O, Johannsen, Anders
We consider a new perspective on dialog state tracking (DST), the task of estimating a user's goal through the course of a dialog. By formulating DST as a semantic parsing task over hierarchical representations, we can incorporate semantic compositio
Externí odkaz:
http://arxiv.org/abs/2010.12770
Autor:
Bhargava, Shruti, Forsyth, David
The task of image captioning implicitly involves gender identification. However, due to the gender bias in data, gender identification by an image captioning model suffers. Also, the gender-activity bias, owing to the word-by-word prediction, influen
Externí odkaz:
http://arxiv.org/abs/1912.00578
Autor:
Fanti, Giulia, Venkatakrishnan, Shaileshh Bojja, Bakshi, Surya, Denby, Bradley, Bhargava, Shruti, Miller, Andrew, Viswanath, Pramod
Recent work has demonstrated significant anonymity vulnerabilities in Bitcoin's networking stack. In particular, the current mechanism for broadcasting Bitcoin transactions allows third-party observers to link transactions to the IP addresses that or
Externí odkaz:
http://arxiv.org/abs/1805.11060