Schema Collision Detection and LLM-Based Semantic Correction
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Solution Overview
Problem
Large language models (LLMs) struggle with semantic collisions and ambiguities in formalized data representation languages, leading to degraded performance in dense retrieval tasks and reduced precision in query result retrieval, especially in complex environments.
Innovation Solution
A method and system that utilize an embedding model to generate embeddings for schema elements, group them into categories, detect semantic collisions, and resolve these collisions using a large language model (LLM) to update schema elements, enhancing descriptions and normalizing schema elements to improve semantic clarity and retrieval accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If formalized schemas are used in software application ecosystems, then structured data access and manipulation are facilitated, but semantic collisions and ambiguities arise that degrade LLM performance
Solution Approach 1:
The system performs preliminary semantic collision detection and resolution on schemas before they are used by LLMs. By pre-processing schemas to identify and resolve semantic collisions, ambiguities, and inconsistencies beforehand, the system ensures that LLMs receive clean, unambiguous schema information, thereby improving reliability without causing information loss
Solution Approach 2:
The system introduces an intermediary processing layer between the formalized schemas and the LLMs. This intermediary component detects semantic collisions, generates collision reports, and resolves ambiguities before presenting schemas to LLMs, acting as a mediator that preserves semantic clarity while maintaining structured data representation
2Measurement precision
If schema elements are densely defined to improve data representation, then retrieval precision should improve, but semantic collisions increase causing degraded performance
Solution Approach 1:
The system converts the harmful effect of semantic collisions into a beneficial detection and resolution process. By systematically identifying semantic collisions through embedding comparison and using LLMs to resolve them, the system transforms what would be harmful ambiguities into opportunities for schema improvement, thereby maintaining high retrieval precision without the negative effects of semantic collisions
3Loss of information
If schema documentation is made more detailed to improve contextualization, then LLM reasoning should improve, but semantic duplication and inconsistent naming conventions increase
Solution Approach 1:
The system applies local quality improvement by resolving semantic collisions at the individual schema element level rather than requiring global schema simplification. By detecting and resolving ambiguities, semantic duplications, and naming inconsistencies in specific locations within the schema, the system maintains detailed contextual information where needed while reducing overall schema complexity through targeted improvements
Data Source
AI summary
A method includes constructing a schema segment from a schema segment definition. The schema segment includes a multitude of schema elements. A multitude of embeddings corresponding to the multitude of schema elements is generated by an embedding model. The multitude of schema elements and the multitude of embeddings is grouped into a multitude of categories. A semantic collision between a first embedding of a first schema element and a second embedding of a second schema element of a category of the multitude of categories may be detected. A set of semantic collisions may be obtained. A large language model (LLM) may resolve the set of semantic collisions to obtain a corresponding multitude of updated schema elements. The schema segment is updated with the corresponding multitude of updated schema elements.