Synthetic Tabular Metadata Generation With LLM Context Retrieval
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Solution Overview
Problem
Existing natural language processing techniques struggle with accurately generating metadata for tabular data due to ambiguous or unfamiliar notations, such as acronyms and abbreviations, leading to semantic inaccuracies and computational inefficiencies.
Innovation Solution
A novel approach using a large language model (LLM) with Retrieval Augmented Generation (RAG) to dynamically generate structured metadata by leveraging public and proprietary documentation, incorporating in-context learning and exemplars to infer accurate table and column descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional natural language processing techniques are used to generate metadata for tabular data, then the process is computationally efficient, but semantic accuracy deteriorates due to ambiguous notations and acronyms
Solution Approach 1:
The system performs preliminary actions by retrieving relevant context information from knowledge bases and documentation before the metadata generation process. This pre-fetching of contextual data (acronym expansions, domain-specific terminology, related concepts) enables the LLM to generate semantically accurate metadata without requiring extensive computational exploration during the generation phase, thus resolving the contradiction between accuracy and latency
Solution Approach 2:
The system introduces an intermediary retrieval layer that acts as a mediator between the input tabular data and the LLM generator. This intermediary component fetches contextual information from external knowledge bases and proprietary documentation, providing the LLM with pre-processed, context-enriched input that significantly improves semantic accuracy while keeping the LLM's computational burden manageable
2Measurement precision
If comprehensive documentation is searched to identify table and column descriptions, then semantic accuracy improves, but the complexity of the process increases
Solution Approach 1:
The system segments the metadata generation process into distinct modular components: a retrieval module that fetches contextual information from documentation and knowledge bases, and a generation module (LLM) that synthesizes metadata based on retrieved context. This segmentation allows each component to specialize in its function, improving semantic fidelity through comprehensive documentation search while managing process complexity through clear separation of concerns
Solution Approach 2:
The retrieval augmented generation framework introduces an intermediary retrieval layer that mediates between the input data and the LLM. This intermediary component handles the complexity of searching comprehensive documentation by pre-processing and filtering relevant information, then passing only essential contextual data to the LLM, thus improving semantic fidelity without proportionally increasing overall process complexity
3Loss of information
If LLMs are used to generatively infer table schemas with natural language descriptions, then metadata completeness improves, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary retrieval of contextual information from knowledge bases and documentation before invoking the LLM. This pre-fetching of relevant context (acronym definitions, domain terminology, related table schemas) enables the LLM to generate complete and accurate metadata with fewer computational iterations, reducing processor time while maintaining metadata completeness
Solution Approach 2:
The retrieval component acts as an intermediary that prepares and pre-processes contextual information before it reaches the LLM. This intermediary layer filters and structures relevant data from comprehensive documentation, enabling the LLM to focus computational resources on generative tasks rather than information gathering, thus achieving metadata completeness with reduced processor time
Data Source
AI summary
In an embodiment, a computer generates a lexical prompt for a large language model (LLM) that accepts the prompt as input, which causes the LLM to generatively infer a hybrid table schema that contains natural language that describes a data table. The prompt may contain linguistic exemplar(s) that are generated from statically or dynamically selected predefined data tables. As discussed herein, task accuracy of computer inferencing is increased by novel static exemplar(s), and semantic accuracy of computer inferencing is increased by novel dynamic selection of most semantically similar dynamic exemplar(s). Dynamic selection of exemplars is accelerated by indexing of learned semantic vector encodings of predefined and new data tables.