Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Akkiraju, Rama"'
Overcoming the limited context limitations in early-generation LLMs, retrieval-augmented generation (RAG) has been a reliable solution for context-based answer generation in the past. Recently, the emergence of long-context LLMs allows the models to
Externí odkaz:
http://arxiv.org/abs/2409.01666
Autor:
Akkiraju, Rama, Xu, Anbang, Bora, Deepak, Yu, Tan, An, Lu, Seth, Vishal, Shukla, Aaditya, Gundecha, Pritam, Mehta, Hridhay, Jha, Ashwin, Raj, Prithvi, Balasubramanian, Abhinav, Maram, Murali, Muthusamy, Guru, Annepally, Shivakesh Reddy, Knowles, Sidney, Du, Min, Burnett, Nick, Javiya, Sean, Marannan, Ashok, Kumari, Mamta, Jha, Surbhi, Dereszenski, Ethan, Chakraborty, Anupam, Ranjan, Subhash, Terfai, Amina, Surya, Anoop, Mercer, Tracey, Thanigachalam, Vinodh Kumar, Bar, Tamar, Krishnan, Sanjana, Kilaru, Samy, Jaksic, Jasmine, Algarici, Nave, Liberman, Jacob, Conway, Joey, Nayyar, Sonu, Boitano, Justin
Enterprise chatbots, powered by generative AI, are emerging as key applications to enhance employee productivity. Retrieval Augmented Generation (RAG), Large Language Models (LLMs), and orchestration frameworks like Langchain and Llamaindex are cruci
Externí odkaz:
http://arxiv.org/abs/2407.07858
Scaling existing applications and solutions to multiple human languages has traditionally proven to be difficult, mainly due to the language-dependent nature of preprocessing and feature engineering techniques employed in traditional approaches. In t
Externí odkaz:
http://arxiv.org/abs/1912.13106
Autor:
Akkiraju, Rama, Sinha, Vibha, Xu, Anbang, Mahmud, Jalal, Gundecha, Pritam, Liu, Zhe, Liu, Xiaotong, Schumacher, John
Academic literature on machine learning modeling fails to address how to make machine learning models work for enterprises. For example, existing machine learning processes cannot address how to define business use cases for an AI application, how to
Externí odkaz:
http://arxiv.org/abs/1811.04871
Artificial Intelligence (AI) has burrowed into our lives in various aspects; however, without appropriate testing, deployed AI systems are often being criticized to fail in critical and embarrassing cases. Existing testing approaches mainly depend on
Externí odkaz:
http://arxiv.org/abs/1810.09030
In the last several years, Twitter is being adopted by the companies as an alternative platform to interact with the customers to address their concerns. With the abundance of such unconventional conversation resources, push for developing effective
Externí odkaz:
http://arxiv.org/abs/1807.06107
Autor:
Hu, Tianran, Xu, Anbang, Liu, Zhe, You, Quanzeng, Guo, Yufan, Sinha, Vibha, Luo, Jiebo, Akkiraju, Rama
Chatbot has become an important solution to rapidly increasing customer care demands on social media in recent years. However, current work on chatbot for customer care ignores a key to impact user experience - tones. In this work, we create a novel
Externí odkaz:
http://arxiv.org/abs/1803.02952
Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we deve
Externí odkaz:
http://arxiv.org/abs/1709.05413
Autor:
Arnoux, Pierre-Hadrien, Xu, Anbang, Boyette, Neil, Mahmud, Jalal, Akkiraju, Rama, Sinha, Vibha
Predicting personality is essential for social applications supporting human-centered activities, yet prior modeling methods with users written text require too much input data to be realistically used in the context of social media. In this work, we
Externí odkaz:
http://arxiv.org/abs/1704.05513
Autor:
Keskinocak, Pinar, Wu, Frederick, Goodwin, Richard, Murthy, Sesh, Akkiraju, Rama, Kumaran, Santhosh, Derebail, Annap
Publikováno v:
Operations Research, 2002 Mar 01. 50(2), 249-259.
Externí odkaz:
https://www.jstor.org/stable/3088493
