LLM Agent Prompt Generation for Dynamic Role and Value Alignment
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Solution Overview
Problem
Existing large language model (LLM) development methods face challenges in achieving both effective role-play and value alignment, particularly in open-field conversational AI applications, where existing approaches are either data-intensive or too rigid, making deployment difficult.
Innovation Solution
A framework for joint configuration of role and value alignment in LLM agents, utilizing automatic role and alignment configuration modules to adapt to user inputs, incorporating semantic extraction, meta-learning, and interactive feedback to generate prompts that align with user expectations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing approaches for role adaptation are used, then role-play effectiveness is improved, but data requirements and computational resources increase significantly
Solution Approach 1:
The system performs preliminary configuration of role information and alignment information before actual LLM interaction. Role definitions, value alignments, and behavioral guidelines are pre-established in structured formats, allowing the LLM to adapt to new roles without requiring extensive training data or computational resources during deployment
Solution Approach 2:
The system enables dynamic role adaptation where the LLM can switch between different roles and alignment configurations based on user input. The framework allows real-time modification of role information and alignment information without retraining, making the system flexible and adaptable to diverse scenarios
2Measurement precision
If existing approaches for value alignment are used, then alignment accuracy is improved, but system rigidity increases making deployment difficult
Solution Approach 1:
The system segments value alignment into modular components including role information, alignment information, and configuration parameters. Each component can be independently configured, modified, and combined, allowing high alignment accuracy through structured design while maintaining deployment flexibility through modular assembly
Solution Approach 2:
The framework creates a universal configuration system that can handle multiple roles, values, and scenarios through a unified prompt generation mechanism. The same core infrastructure supports diverse alignment requirements by dynamically assembling appropriate role and alignment information from standardized components
3Reliability
If complex role configuration is implemented, then role-play quality is improved, but system complexity increases
Solution Approach 1:
The system introduces intermediary components including a role information configuration module, an alignment information configuration module, and a prompt generation module that mediate between complex role requirements and LLM execution. These intermediaries structure and organize complex configuration data into standardized formats, simplifying the overall system architecture while enabling high-quality role-play
Data Source
AI summary
Embodiments of the present disclosure provide a method, an electronic device, and a program product for a large language model (LLM). The method includes: receiving a user input for an LLM agent; determining role information and user-related alignment information for the LLM agent based on the user input; generating a prompt including the role information and the alignment information; and generating an answer to the user input by providing the prompt to the LLM. In this way, appropriate role and user-related alignment information can be configured for the LLM agent to help the LLM agent to provide a desired answer for users in open application fields, thereby improving user experience.