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of 5
pro vyhledávání: '"Rallabandi, SaiKrishna"'
Autor:
Dakle, Parag Pravin, Gon, Alolika, Zha, Sihan, Wang, Liang, Rallabandi, SaiKrishna, Raghavan, Preethi
In this paper, we describe the different approaches explored by the Jetsons team for the Multi-Lingual ESG Impact Duration Inference (ML-ESG-3) shared task. The shared task focuses on predicting the duration and type of the ESG impact of a news artic
Externí odkaz:
http://arxiv.org/abs/2404.00386
This study delves into the capabilities and limitations of Large Language Models (LLMs) in the challenging domain of conditional question-answering. Utilizing the Conditional Question Answering (CQA) dataset and focusing on generative models like T5
Externí odkaz:
http://arxiv.org/abs/2312.01143
Autor:
Wu, Yijing, Rallabandi, SaiKrishna, Srinivasamurthy, Ravisutha, Dakle, Parag Pravin, Gon, Alolika, Raghavan, Preethi
Spoken question answering (SQA) systems are critical for digital assistants and other real-world use cases, but evaluating their performance is a challenge due to the importance of human-spoken questions. This study presents a new large-scale communi
Externí odkaz:
http://arxiv.org/abs/2304.13689
We view the landscape of large language models (LLMs) through the lens of the recently released BLOOM model to understand the performance of BLOOM and other decoder-only LLMs compared to BERT-style encoder-only models. We achieve this by evaluating t
Externí odkaz:
http://arxiv.org/abs/2211.14865
Autor:
Rallabandi, SaiKrishna
Latest addition to the toolbox of human species is Artificial Intelligence(AI). Thus far, AI has made significant progress in low stake low risk scenarios such as playing Go and we are currently in a transition toward medium stake scenarios such as V
Externí odkaz:
http://arxiv.org/abs/1909.09964