Identifier-Attribute Mapping for Domain-Specific LLM Queries
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Solution Overview
Problem
Large language models (LLMs) often lack domain-specific knowledge and confuse domain-specific terminology due to broader interpretations of terms across domains, leading to incorrect entity identification in queries for specialized tasks like querying IoT devices, where compound identifiers result in inaccurate database queries.
Innovation Solution
A framework that replaces identifiers in natural language queries with placeholder attributes using identifier-attribute mappings, leveraging retrieval augmented generation (RAG) to generate accurate database queries by prompting a large language model with attribute-based queries, and then replacing attributes with identifiers to retrieve relevant data from a database.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs are used for general natural language tasks, then they have large breadth of knowledge, but they lack domain-specific knowledge and confuse domain-specific terminology
Solution Approach 1:
The patent segments the query processing into two distinct parts: (1) identifier detection and replacement with attributes, and (2) LLM processing. This segmentation allows the LLM to handle general language understanding while external mapping tables handle domain-specific terminology, resolving the contradiction between general knowledge and domain-specific accuracy
Solution Approach 2:
The patent introduces mapping tables as an intermediary component between the LLM and the domain-specific database. These mapping tables store identifier-attribute mappings and act as a mediator that translates domain-specific terminology into attributes the LLM can process accurately, thereby maintaining both general language capability and domain-specific precision
2Device complexity
If LLMs directly process identifier-based natural language queries, then the system is simple, but entity identification becomes inaccurate due to compound identifiers
Solution Approach 1:
The patent applies preliminary action by performing identifier-to-attribute replacement before the LLM processes the query. The system pre-processes the natural language query by detecting identifiers and replacing them with corresponding attributes from mapping tables, ensuring accurate entity identification before the LLM generates the database query
Solution Approach 2:
The patent substitutes the LLM's direct interpretation of identifiers with a deterministic lookup mechanism in mapping tables. Instead of relying on the LLM to correctly interpret compound identifiers through probabilistic language understanding, the system uses exact matching in pre-populated mapping tables to replace identifiers with unambiguous attributes
Data Source
AI summary
A contextual natural language query response system (contextual system) leverages contextual attribute-identifier mappings to improve large language model (LLM) responses to natural language queries. The contextual system replaces identifiers in natural language queries with attributes according to a contextual mapping table between identifiers and attributes to generate attribute-based natural language queries. The contextual system then uses retrieval-augmented generation with the attributes-based natural language queries to prompt an LLM to generate attribute-based database queries. The contextual system uses the mappings from the contextual mapping table to convert the attribute-based database queries to identifier-based database queries and queries a database with the identifier-based database queries. The contextual system responses to the natural language queries using results from querying the database.