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pro vyhledávání: '"Dakle P"'
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
Many existing end-to-end systems for hybrid question answering tasks can often be boiled down to a "prompt-and-pray" paradigm, where the user has limited control and insight into the intermediate reasoning steps used to achieve the final result. Addi
Externí odkaz:
http://arxiv.org/abs/2402.17882
Autor:
Su Lin Lim, Alisa Damnernsawad, Pavithra Shyamsunder, Wee Joo Chng, Bing Chen Han, Liang Xu, Jian Pan, Dakle Pushkar Pravin, Serhan Alkan, Jeffrey W. Tyner, H. Phillip Koeffler
Publikováno v:
Haematologica, Vol 104, Iss 6 (2019)
Proteolysis targeting chimeric molecule ARV 825 causes ubiquitination of bromodomains resulting in their efficient degradation by proteasome activity. Bromodomain degradation down-regulates MYC transcription contributing to growth inhibition of vario
Externí odkaz:
https://doaj.org/article/9021855a10e84e4687b818197b0e2f3b
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
Sentiment analysis is a crucial task in natural language processing that involves identifying and extracting subjective sentiment from text. Self-training has recently emerged as an economical and efficient technique for developing sentiment analysis
Externí odkaz:
http://arxiv.org/abs/2309.08777
In addressing the task of converting natural language to SQL queries, there are several semantic and syntactic challenges. It becomes increasingly important to understand and remedy the points of failure as the performance of semantic parsing systems
Externí odkaz:
http://arxiv.org/abs/2305.19974
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:
Dakle, Parag Pravin, Moldovan, Dan I.
Publikováno v:
Proceedings of the 28th International Conference on Computational Linguistics, pp. 339-349. 2020
We present the first large scale corpus for entity resolution in email conversations (CEREC). The corpus consists of 6001 email threads from the Enron Email Corpus containing 36,448 email messages and 60,383 entity coreference chains. The annotation
Externí odkaz:
http://arxiv.org/abs/2105.10606
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