LLM Response Interface With Character-Based Conversation Control
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Solution Overview
Problem
Existing response output techniques using artificial intelligence do not adequately consider user interaction and conversation characteristics, leading to suboptimal user experience.
Innovation Solution
A response output apparatus equipped with an input interface, controller, storage, and output interface that interacts with a large language model server, generating prompts, sending control information, and outputting responses based on user input, while incorporating conversation characteristics settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a large language model application is used to generate responses, then the response capability is improved, but the conversation characteristics and user interaction quality deteriorate
Solution Approach 1:
A character information management unit is introduced as an intermediary between the large language model application and the user. This unit stores and manages character information including conversation characteristics, and the character information setting unit allows users to customize these characteristics. The large language model application generates responses based on both the user input and the stored character information, thereby maintaining automated response capability while improving conversation quality and adaptability through the mediating role of character information management.
2Device complexity
If a simple response output system is used, then the device complexity is reduced, but the user experience quality deteriorates
Solution Approach 1:
The response output system is segmented into distinct functional units: a large language model application for generating responses, a character information management unit for storing and managing character information including conversation characteristics, and a character information setting unit for user customization. This segmentation allows the system to maintain relatively simple individual components while achieving enhanced user experience through their coordinated interaction, resolving the contradiction between device complexity and user experience quality.
Data Source
AI summary
The controller is capable of executing a client application that can exchange information with a large language model application that controls a large language model on a server external to the response output apparatus or stored in the response output apparatus. The client application is capable of generating a prompt for the large language model based on the user input received via the input interface, sending control information that differs from the prompt to the large language model application, sending the prompt to the large language model application, receiving a response phrase that is a result of inference executed by the large language model from the large language model application, and outputting a response based on the response phrase to the user via the output interface. The storage is configured to store settings related to conversation characteristics of a character.