IQLS: Framework for leveraging Metadata to enable Large Language Model based queries to complex, versatile Data
<|reference_start|>IQLS: Framework for leveraging Metadata to enable Large Language Model based queries to complex, versatile Data: As the amount and complexity of data grows, retrieving it has become a more difficult task that requires greater knowledge and resources. This is especially true for the logistics industry, where new technologies for data collection provide tremendous amounts of interconnected real-time data. The Intelligent Query and Learning System (IQLS) simplifies the process by allowing natural language use to simplify data retrieval . It maps structured data into a framework based on the available metadata and available data models. This framework creates an environment for an agent powered by a Large Language Model. The agent utilizes the hierarchical nature of the data to filter iteratively by making multiple small context-aware decisions instead of one-shot data retrieval. After the Data filtering, the IQLS enables the agent to fulfill tasks given by the user query through interfaces. These interfaces range from multimodal transportation information retrieval to route planning under multiple constraints. The latter lets the agent define a dynamic object, which is determined based on the query parameters. This object represents a driver capable of navigating a road network. The road network is depicted as a graph with attributes based on the data. Using a modified version of the Dijkstra algorithm, the optimal route under the given constraints can be determined. Throughout the entire process, the user maintains the ability to interact and guide the system. The IQLS is showcased in a case study on the Canadian logistics sector, allowing geospatial, visual, tabular and text data to be easily queried semantically in natural language.<|reference_end|>
@article{azirar2024iqls:,
    title={IQLS: Framework for leveraging Metadata to enable Large Language Model
  based queries to complex, versatile Data},
    author={Sami Azirar, Hossam A. Gabbar, Chaouki Regoui},
    journal={arXiv preprint arXiv:2405.15792},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.15792},
    primaryClass={cs.IR cs.AI}
}