LLM Query-Response Pair Generation for Unstructured Data Retrieval
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Solution Overview
Problem
Existing data mining approaches for user query resolution in unstructured data fail to effectively identify relevant data across multiple, disparate memory locations without metadata, leading to loss of context and inefficient resource usage.
Innovation Solution
A two-phase system using a first LLM to generate query-response pairs from unstructured data and store them as embeddings in a knowledge base, allowing a second LLM to respond to user queries semantically, with a feedback loop for correction and evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is stored at multiple disparate memory locations without metadata, then data availability is improved, but data retrieval efficiency deteriorates
Solution Approach 1:
The system performs preliminary action by generating query-response pairs from unstructured data before actual queries are made. LLMs synthesize the data into structured Q&A pairs and store them as embeddings in a knowledge base, so when a user query arrives, the system can quickly retrieve relevant pre-processed information without searching through raw unstructured data at multiple memory locations.
2Measurement precision
If unstructured data is synthesized into query-response pairs, then response accuracy is improved, but storage resource usage increases
Solution Approach 1:
The system creates simplified copies of unstructured data in the form of query-response pairs with embeddings. Instead of storing and processing the complete original unstructured data, the system generates condensed representations (embeddings) that capture the essential meaning and relationships, allowing efficient storage and retrieval while maintaining response accuracy.
3Measurement precision
If LLMs are used to process unstructured data, then response quality is improved, but computing resource consumption increases
Solution Approach 1:
The system performs computationally intensive LLM processing in advance during the offline phase to generate query-response pairs and embeddings. This preliminary computation transforms unstructured data into a structured format that can be quickly retrieved and used during online query processing, shifting the heavy computing burden from real-time query handling to pre-processing.
Solution Approach 2:
The system extracts only the essential information from unstructured data by generating representative query-response pairs and their embeddings. Instead of processing and storing all original data, the system extracts key information that answers potential user queries, reducing the computational load during actual query processing while maintaining response quality.
Data Source
AI summary
An unstructured data query-response pair generation system (generation system) populates a knowledge base of query-response pairs for queries of natural language content in unstructured data by prompting a first large language model (LLM) text extracted from the unstructured data. An unstructured data chatbot (chatbot) leverages the knowledge base by augmenting prompts to a second LLM responding to user queries for natural language content in the unstructured data with query-response pairs having queries that are semantically similar to the user queries. The knowledge base and LLMs are updated based on user feedback correcting responses, continually improving quality of the generation system and chatbot.