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pro vyhledávání: '"Mavi, Vaibhav"'
Applying existing question answering (QA) systems to specialized domains like law and finance presents challenges that necessitate domain expertise. Although large language models (LLMs) have shown impressive language comprehension and in-context lea
Externí odkaz:
http://arxiv.org/abs/2310.14435
The task of Question Answering (QA) has attracted significant research interest for long. Its relevance to language understanding and knowledge retrieval tasks, along with the simple setting makes the task of QA crucial for strong AI systems. Recent
Externí odkaz:
http://arxiv.org/abs/2204.09140
The task of automatic text summarization has gained a lot of traction due to the recent advancements in machine learning techniques. However, evaluating the quality of a generated summary remains to be an open problem. The literature has widely adopt
Externí odkaz:
http://arxiv.org/abs/2201.09282
The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models. In this manuscript, we propose an extractor-paraphraser based abstractive summarization system that exploi
Externí odkaz:
http://arxiv.org/abs/2105.01296
Publikováno v:
Foundations & Trends in Information Retrieval; 2024, Vol. 17 Issue 5, p457-586, 130p
Akademický článek
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The problem of Question Answering (QA) has attracted significant research interest for long. Its relevance to language understanding and knowledge retrieval tasks, along with the simple setting makes the task of QA crucial for strong AI systems. Rece
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1c0a9440be10897841205fef9b3ed751
http://arxiv.org/abs/2204.09140
http://arxiv.org/abs/2204.09140