Language Model Extracting Q&A Pairs for Knowledge Base Enrichment
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Solution Overview
Problem
Current chatbot systems for customer support rely heavily on manually curated knowledge bases, which are inefficient and resource-intensive, as most customer support data is stored in conversations between customers and support agents, not in help centers.
Innovation Solution
The use of a language model to programmatically identify and extract conversational snippets from customer support interactions, which can then be used to enrich or create a knowledge base for chatbots, allowing for automated customer support.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually curated knowledge bases are used for customer support chatbots, then the knowledge base can be accurately maintained, but the process is inefficient and resource-intensive
Solution Approach 1:
The system enables self-service by automatically extracting Q&A pairs from conversation logs and enriching the knowledge base without requiring manual intervention. The machine learning model processes conversation data, identifies relevant Q&A pairs, and updates the knowledge base autonomously, transforming a manual process into an automated one that maintains accuracy while significantly improving efficiency.
Solution Approach 2:
The patent replaces the mechanical manual process of curating and updating the knowledge base with an automated machine learning-based system. The machine learning model analyzes conversation logs, extracts meaningful Q&A pairs, and integrates them into the knowledge base, substituting human labor with an intelligent automated system that achieves both accuracy and efficiency.
2Measurement precision
If manually analysis and editing of conversational data is performed, then data quality can be controlled, but computational and network resources are consumed inefficiently
Solution Approach 1:
The system performs self-service by automatically analyzing and editing conversational data through machine learning models. The model independently processes conversation logs, identifies high-quality Q&A pairs, and prepares them for knowledge base integration without requiring human computational resources, thereby maintaining data quality while eliminating inefficient manual resource consumption.
Solution Approach 2:
The patent substitutes the manual mechanical process of data analysis and editing with an automated machine learning system. The machine learning model efficiently processes large volumes of conversational data, filters for quality, and extracts relevant information, replacing human computational effort with an intelligent system that achieves superior resource efficiency.
3Loss of information
If conversational data is manually processed to enrich knowledge base, then relevant information can be extracted, but the process is time-consuming
Solution Approach 1:
The system enables self-service by automatically extracting relevant information from conversational data through machine learning. The model processes conversation logs in real-time or batch mode, identifies meaningful Q&A pairs, and enriches the knowledge base autonomously, achieving complete information extraction without the time-consuming manual processing that would otherwise be required.
Solution Approach 2:
The patent replaces the time-consuming manual information extraction process with an automated machine learning system. The machine learning model efficiently analyzes conversational data, extracts relevant Q&A pairs, and integrates them into the knowledge base, achieving both completeness of information extraction and significant reduction in processing time through intelligent automation.
Data Source
AI summary
Technology is disclosed for programmatically controlling a language model to extract aspects of conversations. In one implementation, a conversation is accessed from a communication record between a user and support agent. A first prompt is generated for a language model with the communication record and a request to the language model to extract each of a set of questions and answer (“Q&A”) pairs from the communication record and provide contextual metadata corresponding to each Q&A pair. A second prompt is generated for the language model with a request to the language model to filter irrelevant Q&A pairs based on the contextual metadata of each Q&A pair and generate a single, summarized Q&A pair based on each remaining Q&A pair. An embedding corresponding to at least a portion of the single, summarized Q&A pair is generated, and may be utilized for knowledge base enrichment or for deploying an improved chatbot.