Search

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.

Abstract Citations are widely considered in scientists’ evaluation. As such, scientists may be incentivized to inflate their citation counts. While previous literature has examined self-citations and citation cartels, it remains unclear whether scientists can purchase citations. Here, we compile a dataset of ~1.6 million profiles on Google Scholar to examine instances of citation fraud on the platform. We survey faculty at highly-ranked universities, and confirm that Google Scholar is widely used when evaluating scientists. We then engage with a citation-boosting service, and manage to purchase 50 citations while assuming the identity of a fictional author. Taken as a whole, our findings bring to light new forms of citation manipulation, and emphasize the need to look beyond citation counts.