Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Patel, Siva Sankalp"'
Autor:
Fadnis, Kshitij, Patel, Siva Sankalp, Boni, Odellia, Katsis, Yannis, Rosenthal, Sara, Sznajder, Benjamin, Danilevsky, Marina
Large Language Models (LLM) have become a popular approach for implementing Retrieval Augmented Generation (RAG) systems, and a significant amount of effort has been spent on building good models and metrics. In spite of increased recognition of the
Externí odkaz:
http://arxiv.org/abs/2404.17347
Autor:
Li, Raymond, Allal, Loubna Ben, Zi, Yangtian, Muennighoff, Niklas, Kocetkov, Denis, Mou, Chenghao, Marone, Marc, Akiki, Christopher, Li, Jia, Chim, Jenny, Liu, Qian, Zheltonozhskii, Evgenii, Zhuo, Terry Yue, Wang, Thomas, Dehaene, Olivier, Davaadorj, Mishig, Lamy-Poirier, Joel, Monteiro, João, Shliazhko, Oleh, Gontier, Nicolas, Meade, Nicholas, Zebaze, Armel, Yee, Ming-Ho, Umapathi, Logesh Kumar, Zhu, Jian, Lipkin, Benjamin, Oblokulov, Muhtasham, Wang, Zhiruo, Murthy, Rudra, Stillerman, Jason, Patel, Siva Sankalp, Abulkhanov, Dmitry, Zocca, Marco, Dey, Manan, Zhang, Zhihan, Fahmy, Nour, Bhattacharyya, Urvashi, Yu, Wenhao, Singh, Swayam, Luccioni, Sasha, Villegas, Paulo, Kunakov, Maxim, Zhdanov, Fedor, Romero, Manuel, Lee, Tony, Timor, Nadav, Ding, Jennifer, Schlesinger, Claire, Schoelkopf, Hailey, Ebert, Jan, Dao, Tri, Mishra, Mayank, Gu, Alex, Robinson, Jennifer, Anderson, Carolyn Jane, Dolan-Gavitt, Brendan, Contractor, Danish, Reddy, Siva, Fried, Daniel, Bahdanau, Dzmitry, Jernite, Yacine, Ferrandis, Carlos Muñoz, Hughes, Sean, Wolf, Thomas, Guha, Arjun, von Werra, Leandro, de Vries, Harm
The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilitie
Externí odkaz:
http://arxiv.org/abs/2305.06161
Autor:
Murthy V, Rudra, Bhat, Riyaz, Gunasekara, Chulaka, Patel, Siva Sankalp, Wan, Hui, Dhamecha, Tejas Indulal, Contractor, Danish, Danilevsky, Marina
In this paper we explore the task of modeling semi-structured object sequences; in particular, we focus our attention on the problem of developing a structure-aware input representation for such sequences. Examples of such data include user activity
Externí odkaz:
http://arxiv.org/abs/2301.01015
Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available. The state-of-the-art technology
Externí odkaz:
http://arxiv.org/abs/2205.14226
Publikováno v:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given documen
Externí odkaz:
http://arxiv.org/abs/2109.12595
Autor:
Feng, Song, Wan, Hui, Gunasekara, Chulaka, Patel, Siva Sankalp, Joshi, Sachindra, Lastras, Luis A.
We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents. Inspired by how the authors compose documents for guiding end users, we first construct dialogue flows based on the content elements that c
Externí odkaz:
http://arxiv.org/abs/2011.06623
Autor:
Kapanipathi, Pavan, Thost, Veronika, Patel, Siva Sankalp, Whitehead, Spencer, Abdelaziz, Ibrahim, Balakrishnan, Avinash, Chang, Maria, Fadnis, Kshitij, Gunasekara, Chulaka, Makni, Bassem, Mattei, Nicholas, Talamadupula, Kartik, Fokoue, Achille
Textual entailment is a fundamental task in natural language processing. Most approaches for solving the problem use only the textual content present in training data. A few approaches have shown that information from external knowledge sources like
Externí odkaz:
http://arxiv.org/abs/1911.02060
Goal-oriented dialog systems, which can be trained end-to-end without manually encoding domain-specific features, show tremendous promise in the customer support use-case e.g. flight booking, hotel reservation, technical support, student advising etc
Externí odkaz:
http://arxiv.org/abs/1907.05792
Autor:
Kummerfeld, Jonathan K., Gouravajhala, Sai R., Peper, Joseph, Athreya, Vignesh, Gunasekara, Chulaka, Ganhotra, Jatin, Patel, Siva Sankalp, Polymenakos, Lazaros, Lasecki, Walter S.
Publikováno v:
ACL (2019) 3846-3856
Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs th
Externí odkaz:
http://arxiv.org/abs/1810.11118