Multi-Model Polling for Accurate Automated Technical Support
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Solution Overview
Problem
Existing automated technical support systems using single language models often generate inaccurate or fabricated responses due to a lack of specialized knowledge and context, particularly in addressing user technical queries.
Innovation Solution
A multi-model polling approach that combines outputs from specialized machine-learning language models with a general-purpose model to generate accurate and coherent responses by leveraging vendor-specific knowledge, reducing the likelihood of hallucinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single general-purpose language model is used for automated technical support, then the system is simple to operate and deploy, but the response accuracy and reliability deteriorate due to lack of specialized knowledge
Solution Approach 1:
The system segments the language model functionality into specialized models (each trained on specific vendor or product knowledge) and a general-purpose model. Each specialized model handles specific technical domains while the general model coordinates them, resolving the contradiction by distributing specialized knowledge across multiple components rather than relying on a single general model.
Solution Approach 2:
The general-purpose language model serves multiple functions: it acts as a coordinator that receives user queries, distributes them to appropriate specialized models, aggregates their responses, and presents final answers. This multi-functionality allows the system to maintain simplicity in the coordination layer while incorporating specialized knowledge through multiple specialized models.
2Reliability
If multiple specialized language models are used to improve response accuracy, then the reliability of technical support improves, but the device complexity increases
Solution Approach 1:
The general-purpose language model acts as an intermediary that simplifies the interaction between users and multiple specialized models. It manages the complexity by automatically routing queries to appropriate specialists, aggregating their responses, and presenting unified answers, thereby hiding the underlying system complexity from users while maintaining high response accuracy through specialized knowledge.
3Reliability
If specialized machine-learning models are integrated to reduce hallucinations, then the reliability of technical support improves, but the computational resources and processing time increase
Solution Approach 1:
The system applies partial specialization by having the general-purpose model handle initial query processing and coordination, while only invoking specialized models when specific technical knowledge is needed. This partial application of specialized processing reduces overall computational overhead compared to using multiple specialized models for every query, while still maintaining reliability by engaging specialists for relevant technical questions.
Data Source
AI summary
A method of automated technical support includes receiving a natural-language text prompt provided by a user and including at least one technical query, providing a first system prompt to a primary general-purpose machine-learning language model, and providing the natural-language text prompt to the primary general-purpose machine-learning language model and each of a plurality of specialized machine-learning language models after providing the first system prompt. The method further includes generating a plurality of natural-language text outputs by the plurality of specialized machine-learning language models and the primary general-purpose machine-learning language model, generating an aggregated prompt by combining the plurality of natural-language text outputs, providing a second system prompt to the primary general-purpose machine-learning language model, providing the aggregated prompt to the primary general-purpose machine-learning language model after providing the second system prompt, and generating an orchestrated natural-language text output based on the aggregated prompt by the primary general-purpose machine-learning language model.