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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.13514 |
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| _version_ | 1866909465995902976 |
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| author | Wang, Hongru Xue, Boyang Zhou, Baohang Zhang, Tianhua Wang, Cunxiang Wang, Huimin Chen, Guanhua Wong, Kam-fai |
| author_facet | Wang, Hongru Xue, Boyang Zhou, Baohang Zhang, Tianhua Wang, Cunxiang Wang, Huimin Chen, Guanhua Wong, Kam-fai |
| contents | Previous research has typically concentrated on leveraging the internal knowledge of Large Language Models (LLMs) to answer known questions (i.e., \textit{internal reasoning such as generate-then-read}). In contrast, for questions that fall outside their known scope, these models rely on external knowledge retrieval to provide accurate responses (i.e., \textit{external acting such as retrieve-then-read}). However, few previous works consider the \textit{compositional questions}, which consist of several known and unknown sub-questions, necessitating the dynamic combination of previous two methods (i.e., \textit{internal reasoning and external acting}) to achieve a better trade-off between effectiveness and efficiency. To this end, we introduce a \textbf{Self} \textbf{D}ivide-and-\textbf{C}onquer (\textit{\texttt{Self-DC}}) framework, accompanying with the first \textbf{C}ompositional \textbf{u}nknown \textbf{Q}uestion-\textbf{A}nswering dataset (CuQA). This framework enables LLMs to adaptively choose between using internal knowledge and retrieving external knowledge as needed, resulting in a better trade-off between effectiveness and efficiency. Experimental results on two datasets demonstrate that \textit{\texttt{Self-DC}} can achieve comparable or even better performance with much fewer external calls compared with several strong baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_13514 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions Wang, Hongru Xue, Boyang Zhou, Baohang Zhang, Tianhua Wang, Cunxiang Wang, Huimin Chen, Guanhua Wong, Kam-fai Computation and Language Artificial Intelligence Previous research has typically concentrated on leveraging the internal knowledge of Large Language Models (LLMs) to answer known questions (i.e., \textit{internal reasoning such as generate-then-read}). In contrast, for questions that fall outside their known scope, these models rely on external knowledge retrieval to provide accurate responses (i.e., \textit{external acting such as retrieve-then-read}). However, few previous works consider the \textit{compositional questions}, which consist of several known and unknown sub-questions, necessitating the dynamic combination of previous two methods (i.e., \textit{internal reasoning and external acting}) to achieve a better trade-off between effectiveness and efficiency. To this end, we introduce a \textbf{Self} \textbf{D}ivide-and-\textbf{C}onquer (\textit{\texttt{Self-DC}}) framework, accompanying with the first \textbf{C}ompositional \textbf{u}nknown \textbf{Q}uestion-\textbf{A}nswering dataset (CuQA). This framework enables LLMs to adaptively choose between using internal knowledge and retrieving external knowledge as needed, resulting in a better trade-off between effectiveness and efficiency. Experimental results on two datasets demonstrate that \textit{\texttt{Self-DC}} can achieve comparable or even better performance with much fewer external calls compared with several strong baselines. |
| title | Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2402.13514 |