Multi-Sided LLM Assistant System with Shared Context
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Solution Overview
Problem
Existing large language models (LLMs) limit interactions to a singular assistant, resulting in users receiving a single viewpoint or perspective, which may lead to biased or non-holistic responses due to the assistant's persona, potentially providing incorrect or incomplete information.
Innovation Solution
A system with multiple software assistants interfaces with LLMs, sharing contextual information and directing each LLM to generate content based on its unique persona, allowing for a single conversation to include diverse perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a single assistant interface is used with LLM, then the system is simple and easy to operate, but the response perspective is limited and may be biased
Solution Approach 1:
The patent divides the single assistant interface into multiple assistant interfaces, each with distinct personas. This segmentation allows the system to provide diverse perspectives while maintaining individual assistant simplicity. Each assistant maintains its own persona characteristics while operating within the unified system architecture.
Solution Approach 2:
The patent implements a universal system architecture that supports multiple assistant interfaces with different personas. The core LLM and context management system serve all assistants, enabling multi-functionality where a single system can provide multiple perspectives simultaneously without requiring separate independent systems.
2Adaptability or versatility
If multiple assistants with different personas are used, then response comprehensiveness improves, but system complexity increases
Solution Approach 1:
The patent merges multiple assistant interfaces and their respective personas into a unified system architecture. The context tracker, LLM interface, and conversation management are shared across all assistants, combining their functionalities to reduce overall system complexity while maintaining diverse response capabilities.
Solution Approach 2:
The patent introduces a context tracker as an intermediary component that manages the shared context between multiple assistants and the LLM. This mediator simplifies the system by centralizing context management, allowing multiple assistants to operate without direct coordination complexity.
3Reliability
If multiple assistants share contextual information, then response quality improves, but information processing load increases
Solution Approach 1:
The patent implements preliminary action by pre-establishing persona characteristics and initial contexts for each assistant before user interaction begins. The context tracker pre-organizes shared context information, reducing the processing load during actual user queries by having the framework ready to efficiently distribute and manage context among assistants.
Data Source
AI summary
In an example embodiment, a system is provided having multiple software assistants act as an interface to one or more LLMs. These assistants share contextual information about an ongoing shared conversation, but otherwise direct their respective LLM(s) to generate content based on the assistants' individual personas. The result is that a single conversation can include generated content from one or more LLMs based on multiple different personas.