ICCA-RAG: Intelligent Customs Clearance Assistant Using Retrieval-Augmented Generation (RAG)
Document processing and query generation tasks in customs declaration scenarios face key challenges such as the complexity of multimodal data, adaptability to dynamic regulations, and ambiguity in query semantics. This study proposes a Retrieval-Augmented Generation system (ICCA-RAG) that addresses...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10897992/ |
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| author | Rong Hu Sen Liu Panpan Qi Jingyi Liu Fengyuan Li |
| author_facet | Rong Hu Sen Liu Panpan Qi Jingyi Liu Fengyuan Li |
| author_sort | Rong Hu |
| collection | DOAJ |
| description | Document processing and query generation tasks in customs declaration scenarios face key challenges such as the complexity of multimodal data, adaptability to dynamic regulations, and ambiguity in query semantics. This study proposes a Retrieval-Augmented Generation system (ICCA-RAG) that addresses the core issues of processing complex customs documents and dynamically generating queries through multimodal document parsing, sparse-dense hybrid storage, and context-driven large language model generation. In terms of multimodal document parsing, the system supports comprehensive parsing of PDFs, images, tables, and text, which are uniformly transformed into semantic vectors and keyword indices for hybrid storage. By combining the retrieval and generation modules, the ICCA-RAG system achieves significant improvements in contextual relevance and generation accuracy. Compared to traditional methods, the ICCA-RAG system demonstrates a 20.1% increase in answer correctness, a 15.3% increase in answer relevancy, and an 18.7% increase in the faithfulness of generated content, with outstanding performance in noisy query scenarios. The research findings validate the ICCA-RAG system’s advancement and applicability in handling complex document processing and professional domain question-answering tasks, while also providing a transferable technical framework for other fields, such as law and healthcare. |
| format | Article |
| id | doaj-art-ffd4dd15d0d14dd8a42c8cf485e31eba |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ffd4dd15d0d14dd8a42c8cf485e31eba2025-08-20T03:02:11ZengIEEEIEEE Access2169-35362025-01-0113397113972610.1109/ACCESS.2025.354440810897992ICCA-RAG: Intelligent Customs Clearance Assistant Using Retrieval-Augmented Generation (RAG)Rong Hu0https://orcid.org/0009-0002-9717-042XSen Liu1https://orcid.org/0009-0009-7272-8539Panpan Qi2Jingyi Liu3Fengyuan Li4Customs and Public Management College, Shanghai Customs University, Shanghai, ChinaDepartment of Electronic Information, Shanghai Dianji University, Shanghai, ChinaInformation Department, Xinglin College, Nantong University, Nantong, ChinaSchool of Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaXi’an Jiaotong University, Xi’an, ChinaDocument processing and query generation tasks in customs declaration scenarios face key challenges such as the complexity of multimodal data, adaptability to dynamic regulations, and ambiguity in query semantics. This study proposes a Retrieval-Augmented Generation system (ICCA-RAG) that addresses the core issues of processing complex customs documents and dynamically generating queries through multimodal document parsing, sparse-dense hybrid storage, and context-driven large language model generation. In terms of multimodal document parsing, the system supports comprehensive parsing of PDFs, images, tables, and text, which are uniformly transformed into semantic vectors and keyword indices for hybrid storage. By combining the retrieval and generation modules, the ICCA-RAG system achieves significant improvements in contextual relevance and generation accuracy. Compared to traditional methods, the ICCA-RAG system demonstrates a 20.1% increase in answer correctness, a 15.3% increase in answer relevancy, and an 18.7% increase in the faithfulness of generated content, with outstanding performance in noisy query scenarios. The research findings validate the ICCA-RAG system’s advancement and applicability in handling complex document processing and professional domain question-answering tasks, while also providing a transferable technical framework for other fields, such as law and healthcare.https://ieeexplore.ieee.org/document/10897992/Customs declaration assistancedynamic regulation adaptationintelligent question-answering systemlarge language model (LLM)multimodal document parsingretrieval-augmented generation (RAG) |
| spellingShingle | Rong Hu Sen Liu Panpan Qi Jingyi Liu Fengyuan Li ICCA-RAG: Intelligent Customs Clearance Assistant Using Retrieval-Augmented Generation (RAG) IEEE Access Customs declaration assistance dynamic regulation adaptation intelligent question-answering system large language model (LLM) multimodal document parsing retrieval-augmented generation (RAG) |
| title | ICCA-RAG: Intelligent Customs Clearance Assistant Using Retrieval-Augmented Generation (RAG) |
| title_full | ICCA-RAG: Intelligent Customs Clearance Assistant Using Retrieval-Augmented Generation (RAG) |
| title_fullStr | ICCA-RAG: Intelligent Customs Clearance Assistant Using Retrieval-Augmented Generation (RAG) |
| title_full_unstemmed | ICCA-RAG: Intelligent Customs Clearance Assistant Using Retrieval-Augmented Generation (RAG) |
| title_short | ICCA-RAG: Intelligent Customs Clearance Assistant Using Retrieval-Augmented Generation (RAG) |
| title_sort | icca rag intelligent customs clearance assistant using retrieval augmented generation rag |
| topic | Customs declaration assistance dynamic regulation adaptation intelligent question-answering system large language model (LLM) multimodal document parsing retrieval-augmented generation (RAG) |
| url | https://ieeexplore.ieee.org/document/10897992/ |
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