Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Garera, Nikesh"'
Large Language Models(LLMs) have shown exceptional abilities, yet training these models can be quite challenging. There is a strong dependence on the quality of data and finding the best instruction tuning set. Further, the inherent limitations in tr
Externí odkaz:
http://arxiv.org/abs/2406.19112
Autor:
Muddu, Sri Raghava, Rangaraju, Rupasai, Siledar, Tejpalsingh, Nath, Swaroop, Bhattacharyya, Pushpak, Nath, Swaprava, Banerjee, Suman, Patil, Amey, Chelliah, Muthusamy, Singh, Sudhanshu Shekhar, Garera, Nikesh
Opinion summarization in e-commerce encapsulates the collective views of numerous users about a product based on their reviews. Typically, a product on an e-commerce platform has thousands of reviews, each review comprising around 10-15 words. While
Externí odkaz:
http://arxiv.org/abs/2406.10886
Autor:
Siledar, Tejpalsingh, Rangaraju, Rupasai, Muddu, Sankara Sri Raghava Ravindra, Banerjee, Suman, Patil, Amey, Singh, Sudhanshu Shekhar, Chelliah, Muthusamy, Garera, Nikesh, Nath, Swaprava, Bhattacharyya, Pushpak
In e-commerce, opinion summarization is the process of summarizing the consensus opinions found in product reviews. However, the potential of additional sources such as product description and question-answers (QA) has been considered less often. Mor
Externí odkaz:
http://arxiv.org/abs/2404.05243
Autor:
Nath, Swaroop, Siledar, Tejpalsingh, Muddu, Sankara Sri Raghava Ravindra, Rangaraju, Rupasai, Khadilkar, Harshad, Bhattacharyya, Pushpak, Banerjee, Suman, Patil, Amey, Singh, Sudhanshu Shekhar, Chelliah, Muthusamy, Garera, Nikesh
Reinforcement Learning from Human Feedback (RLHF) has become a dominating strategy in aligning Language Models (LMs) with human values/goals. The key to the strategy is learning a reward model ($\varphi$), which can reflect the latent reward model of
Externí odkaz:
http://arxiv.org/abs/2402.15473
Autor:
Siledar, Tejpalsingh, Nath, Swaroop, Muddu, Sankara Sri Raghava Ravindra, Rangaraju, Rupasai, Nath, Swaprava, Bhattacharyya, Pushpak, Banerjee, Suman, Patil, Amey, Singh, Sudhanshu Shekhar, Chelliah, Muthusamy, Garera, Nikesh
Evaluation of opinion summaries using conventional reference-based metrics rarely provides a holistic evaluation and has been shown to have a relatively low correlation with human judgments. Recent studies suggest using Large Language Models (LLMs) a
Externí odkaz:
http://arxiv.org/abs/2402.11683
Autor:
Joshi, Raviraj, Garera, Nikesh
Text-to-speech (TTS) systems are being built using end-to-end deep learning approaches. However, these systems require huge amounts of training data. We present our approach to built production quality TTS and perform speaker adaptation in extremely
Externí odkaz:
http://arxiv.org/abs/2312.01107
Autor:
Joshi, Raviraj, Garera, Nikesh
Text-to-speech (TTS) systems are an important component in voice-based e-commerce applications. These applications include end-to-end voice assistant and customer experience (CX) voice bot. Code-mixed TTS is also relevant in these applications since
Externí odkaz:
http://arxiv.org/abs/2312.01103
Autor:
Gain, Baban, Appicharla, Ramakrishna, Chennabasavaraj, Soumya, Garera, Nikesh, Ekbal, Asif, Chelliah, Muthusamy
Community Question-Answering (CQA) portals serve as a valuable tool for helping users within an organization. However, making them accessible to non-English-speaking users continues to be a challenge. Translating questions can broaden the community's
Externí odkaz:
http://arxiv.org/abs/2310.15259
Autor:
Goyal, Abhinav, Garera, Nikesh
Automatic Speech Recognition (ASR) plays a crucial role in voice-based applications. For applications requiring real-time feedback like Voice Search, streaming capability becomes vital. While LSTM/RNN and CTC based ASR systems are commonly employed f
Externí odkaz:
http://arxiv.org/abs/2305.18596
Automation of on-call customer support relies heavily on accurate and efficient speech-to-intent (S2I) systems. Building such systems using multi-component pipelines can pose various challenges because they require large annotated datasets, have high
Externí odkaz:
http://arxiv.org/abs/2211.07710