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of 13 314
pro vyhledávání: '"Question answering system"'
Question-answering systems for Bengali have seen limited development, particularly in domain-specific applications. Leveraging advancements in natural language processing, this paper explores a fine-tuned BERT-Bangla model to address this gap. It pre
Externí odkaz:
http://arxiv.org/abs/2410.03923
This paper constructs question answering system for bridge design specification based on large language model. Three implementation schemes are tried: full fine-tuning of the Bert pretrained model, parameter-efficient fine-tuning of the Bert pretrain
Externí odkaz:
http://arxiv.org/abs/2408.13282
Publikováno v:
Science Technology & Engineering. 2024, Vol. 24 Issue 32, p13902-13910. 9p.
In this article, we propose the R2GQA system, a Retriever-Reader-Generator Question Answering system, consisting of three main components: Document Retriever, Machine Reader, and Answer Generator. The Retriever module employs advanced information ret
Externí odkaz:
http://arxiv.org/abs/2409.02840
Autor:
Wang, Zhengren, Yu, Qinhan, Wei, Shida, Li, Zhiyu, Xiong, Feiyu, Wang, Xiaoxing, Niu, Simin, Liang, Hao, Zhang, Wentao
Modern QA systems entail retrieval-augmented generation (RAG) for accurate and trustworthy responses. However, the inherent gap between user queries and relevant documents hinders precise matching. Motivated by our conical distribution hypothesis, wh
Externí odkaz:
http://arxiv.org/abs/2409.20434
In the modern era of rapidly increasing data volumes, accurately retrieving and recommending relevant documents has become crucial in enhancing the reliability of Question Answering (QA) systems. Recently, Retrieval Augmented Generation (RAG) has gai
Externí odkaz:
http://arxiv.org/abs/2409.13699
Autor:
Wang, Qiming, Fernandez, Raul Castro
Reading comprehension models answer questions posed in natural language when provided with a short passage of text. They present an opportunity to address a long-standing challenge in data management: the extraction of structured data from unstructur
Externí odkaz:
http://arxiv.org/abs/2408.09226
Autor:
Ghashami, Mina, Mishra, Soumya Smruti
Publikováno v:
Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
The SemEval 2024 BRAINTEASER task represents a pioneering venture in Natural Language Processing (NLP) by focusing on lateral thinking, a dimension of cognitive reasoning that is often overlooked in traditional linguistic analyses. This challenge com
Externí odkaz:
http://arxiv.org/abs/2405.10385
In today's rapidly evolving landscape of Artificial Intelligence, large language models (LLMs) have emerged as a vibrant research topic. LLMs find applications in various fields and contribute significantly. Despite their powerful language capabiliti
Externí odkaz:
http://arxiv.org/abs/2404.09296
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