Zobrazeno 1 - 10
of 20
pro vyhledávání: '"Karanam, Hima"'
Autor:
Abdelaziz, Ibrahim, Basu, Kinjal, Agarwal, Mayank, Kumaravel, Sadhana, Stallone, Matthew, Panda, Rameswar, Rizk, Yara, Bhargav, GP, Crouse, Maxwell, Gunasekara, Chulaka, Ikbal, Shajith, Joshi, Sachin, Karanam, Hima, Kumar, Vineet, Munawar, Asim, Neelam, Sumit, Raghu, Dinesh, Sharma, Udit, Soria, Adriana Meza, Sreedhar, Dheeraj, Venkateswaran, Praveen, Unuvar, Merve, Cox, David, Roukos, Salim, Lastras, Luis, Kapanipathi, Pavan
Large language models (LLMs) have recently shown tremendous promise in serving as the backbone to agentic systems, as demonstrated by their performance in multi-faceted, challenging benchmarks like SWE-Bench and Agent-Bench. However, to realize the t
Externí odkaz:
http://arxiv.org/abs/2407.00121
Autor:
Bhargav, G P Shrivatsa, Neelam, Sumit, Sharma, Udit, Ikbal, Shajith, Sreedhar, Dheeraj, Karanam, Hima, Joshi, Sachindra, Dhoolia, Pankaj, Garg, Dinesh, Croutwater, Kyle, Qi, Haode, Wayne, Eric, Murdock, J William
We present an approach to build Large Language Model (LLM) based slot-filling system to perform Dialogue State Tracking in conversational assistants serving across a wide variety of industry-grade applications. Key requirements of this system include
Externí odkaz:
http://arxiv.org/abs/2406.08848
Autor:
Neelam, Sumit, Sharma, Udit, Bhatia, Sumit, Karanam, Hima, Likhyani, Ankita, Abdelaziz, Ibrahim, Fokoue, Achille, Subramaniam, L. V.
Resource Description Framework (RDF) and Property Graph (PG) are the two most commonly used data models for representing, storing, and querying graph data. We present Expressive Reasoning Graph Store (ERGS) -- a graph store built on top of JanusGraph
Externí odkaz:
http://arxiv.org/abs/2209.05828
Autor:
Kannen, Nithish, Sharma, Udit, Neelam, Sumit, Khandelwal, Dinesh, Ikbal, Shajith, Karanam, Hima, Subramaniam, L Venkata
Knowledge Base Question Answering (KBQA) systems have the goal of answering complex natural language questions by reasoning over relevant facts retrieved from Knowledge Bases (KB). One of the major challenges faced by these systems is their inability
Externí odkaz:
http://arxiv.org/abs/2203.11054
Autor:
Neelam, Sumit, Sharma, Udit, Karanam, Hima, Ikbal, Shajith, Kapanipathi, Pavan, Abdelaziz, Ibrahim, Mihindukulasooriya, Nandana, Lee, Young-Suk, Srivastava, Santosh, Pendus, Cezar, Dana, Saswati, Garg, Dinesh, Fokoue, Achille, Bhargav, G P Shrivatsa, Khandelwal, Dinesh, Ravishankar, Srinivas, Gurajada, Sairam, Chang, Maria, Uceda-Sosa, Rosario, Roukos, Salim, Gray, Alexander, Lima, Guilherme, Riegel, Ryan, Luus, Francois, Subramaniam, L Venkata
Knowledge Base Question Answering (KBQA) tasks that involve complex reasoning are emerging as an important research direction. However, most existing KBQA datasets focus primarily on generic multi-hop reasoning over explicit facts, largely ignoring o
Externí odkaz:
http://arxiv.org/abs/2201.05793
Autor:
Neelam, Sumit, Sharma, Udit, Karanam, Hima, Ikbal, Shajith, Kapanipathi, Pavan, Abdelaziz, Ibrahim, Mihindukulasooriya, Nandana, Lee, Young-Suk, Srivastava, Santosh, Pendus, Cezar, Dana, Saswati, Garg, Dinesh, Fokoue, Achille, Bhargav, G P Shrivatsa, Khandelwal, Dinesh, Ravishankar, Srinivas, Gurajada, Sairam, Chang, Maria, Uceda-Sosa, Rosario, Roukos, Salim, Gray, Alexander, Riegel, Guilherme LimaRyan, Luus, Francois, Subramaniam, L Venkata
Knowledge Base Question Answering (KBQA) tasks that in-volve complex reasoning are emerging as an important re-search direction. However, most KBQA systems struggle withgeneralizability, particularly on two dimensions: (a) acrossmultiple reasoning ty
Externí odkaz:
http://arxiv.org/abs/2109.13430
Autor:
Kapanipathi, Pavan, Abdelaziz, Ibrahim, Ravishankar, Srinivas, Roukos, Salim, Gray, Alexander, Astudillo, Ramon, Chang, Maria, Cornelio, Cristina, Dana, Saswati, Fokoue, Achille, Garg, Dinesh, Gliozzo, Alfio, Gurajada, Sairam, Karanam, Hima, Khan, Naweed, Khandelwal, Dinesh, Lee, Young-Suk, Li, Yunyao, Luus, Francois, Makondo, Ndivhuwo, Mihindukulasooriya, Nandana, Naseem, Tahira, Neelam, Sumit, Popa, Lucian, Reddy, Revanth, Riegel, Ryan, Rossiello, Gaetano, Sharma, Udit, Bhargav, G P Shrivatsa, Yu, Mo
Knowledge base question answering (KBQA)is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understanding, necessity for reasoning, and lack of large end-to-end training data
Externí odkaz:
http://arxiv.org/abs/2012.01707
Autor:
Riegel, Ryan, Gray, Alexander, Luus, Francois, Khan, Naweed, Makondo, Ndivhuwo, Akhalwaya, Ismail Yunus, Qian, Haifeng, Fagin, Ronald, Barahona, Francisco, Sharma, Udit, Ikbal, Shajith, Karanam, Hima, Neelam, Sumit, Likhyani, Ankita, Srivastava, Santosh
We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning). Every neuron has a meaning as a component of a formula in a weighted real-valued logic, yielding a highly in
Externí odkaz:
http://arxiv.org/abs/2006.13155
Hypernym discovery is the problem of finding terms that have is-a relationship with a given term. We introduce a new context type, and a relatedness measure to differentiate hypernyms from other types of semantic relationships. Our Document Structure
Externí odkaz:
http://arxiv.org/abs/1811.12728
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