LLM Guidance Distillation for Private Domain Question Answering
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Solution Overview
Problem
Existing AI models, both very large and smaller language models, face challenges in generating accurate and comprehensible answers to domain-specific questions due to reliance on immature reasoning and privacy concerns, especially when processing sensitive documents.
Innovation Solution
A method involving a very large language model (VLLM) generates general guidance, updated with domain-specific information, to iteratively guide smaller AI models in answering reasoning questions, ensuring privacy by filtering sensitive documents and utilizing smaller models for faster, cheaper processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If very large language models are used to answer domain-specific questions, then answer accuracy is improved, but processing cost and time increase
Solution Approach 1:
The patent segments the question-answering process into two stages: (1) a very large language model generates general guidance for the domain, and (2) smaller AI models perform iterative question-answering based on that guidance. This segmentation allows the system to achieve high accuracy through the VLLM's domain expertise while avoiding the cost of continuously using it for all processing tasks.
Solution Approach 2:
The patent creates a distilled version of the VLLM's reasoning capabilities by extracting general guidance that captures the essential domain knowledge and reasoning patterns. This guidance acts as a compressed representation that smaller models can execute efficiently, copying the VLLM's intelligent behavior without requiring the VLLM itself to perform all computational tasks.
2Reliability
If very large language models are used to process documents, then reasoning capability is improved, but document privacy risks increase
Solution Approach 1:
The patent extracts only the necessary domain-specific guidance from the VLLM without processing or storing the actual sensitive documents. The VLLM processes documents temporarily to generate guidance, which is then stored and used by smaller models. This extraction approach maintains reasoning capability while minimizing privacy exposure by removing the need to retain original documents in the VLLM system.
Solution Approach 2:
The patent introduces an intermediary layer where the VLLM generates general guidance that serves as a mediator between the sensitive documents and the smaller AI models. This guidance contains the essential reasoning patterns and domain knowledge without exposing the actual document content to the VLLM's permanent storage or processing history, thereby reducing privacy risks.
3Productivity
If smaller AI models are used for processing, then processing cost decreases, but answer accuracy deteriorates
Solution Approach 1:
The patent performs preliminary action by having the VLLM generate comprehensive general guidance that pre-processes and structures the domain knowledge and reasoning approaches. This preliminary guidance preparation enables smaller models to achieve high accuracy by following pre-established reasoning frameworks, eliminating the need for them to independently reason through complex domain questions.
Solution Approach 2:
The patent implements feedback mechanisms where the VLLM iteratively processes and refines the general guidance based on the performance and needs of the smaller models. This feedback loop ensures that the guidance continuously improves and adapts to different question types and document complexities, maintaining high answer accuracy across diverse domains and query patterns.
Data Source
AI summary
Systems and methods for guiding multiple models with a large language model. An instruction code can be generated for a very large language model (VLLM) to generate a general guidance to guide Al models that answer reasoning questions for query documents. The instruction code can be updated with domain-specific information from reference materials to generate, with the VLLM, a reasoned answer for reasoning questions about the query documents generated based on the general guidance. The reasoned answers can be processed into the general guidance with the VLLM. The reasoning question iteratively applied to the query documents can be answered using the general guidance with the Al models to perform downstream tasks.