LLM Agent Principle Optimization for Predictable Action Execution
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Solution Overview
Problem
Existing AI conversation agents, such as chatbots, suffer from inaccuracies in output generation and failure to follow desired task patterns due to hallucinations and lack of real-time information, leading to ineffective task completion.
Innovation Solution
A unified framework is introduced that utilizes a state graph and optimization principles to control LLM agent behavior, ensuring deterministic action execution by defining states and transitions, and dynamically updating the state graph based on operational feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a neural network based generative language model is used to generate output, then the AI agent can produce text responses and actions, but hallucinations and inaccuracies occur leading to failure in completing desired tasks
Solution Approach 1:
A controllable generation module is introduced as an intermediary between the generative language model and the output. This module receives the generated output and verifies it against predefined principles and constraints before final execution, thereby maintaining generative versatility while ensuring accuracy and preventing hallucinations
Solution Approach 2:
The system implements a feedback mechanism where the controllable generation module evaluates generated output against predefined principles. When output violates these principles, feedback is provided to correct or reject the generation, ensuring reliability while preserving the generative capability of the underlying language model
2Productivity
If the AI agent relies on programmed diagnostic steps without real-time external information, then the agent can execute predefined tasks, but the agent misidentifies issues due to lack of real-time information and controllability
Solution Approach 1:
The system transitions from static programmed diagnostic steps to dynamic controllable generation. The controllable generation module can adapt its behavior based on real-time information and predefined principles, allowing the agent to maintain productivity while improving diagnostic accuracy through flexible, context-aware decision-making
Solution Approach 2:
The system changes the parameters of agent behavior by introducing controllable generation with predefined principles. This allows the agent to maintain efficient task execution while improving diagnostic precision through principled control over generation parameters and real-time information integration
3Reliability
If the state graph is dynamically updated based on operational feedback, then the predictability and reliability of LLM agent executions is enhanced, but the complexity of the system increases
Solution Approach 1:
The state graph is pre-defined with predefined principles and constraints before operation. This preliminary structure provides a framework that guides dynamic updates, ensuring that while the system adapts to operational feedback, it maintains predictability and reliability without becoming overly complex
Data Source
AI summary
Embodiments described herein provide an optimization framework to control LLM agent behavior using dynamically optimized principles as part of the generation context. Specifically, a principle may take a form of a set of logic, parameters or text that describe the conditions for using that action. An LLM agent may generate a next step action conditioned on a set of principles corresponding to a set of available actions, and an execution trajectory. A reflector model (such as an LLM) may then generate a reward score based on the generated trajectory and the set of principles. Based on the reward scores, an optimizer (such as an LLM) may revise the set of principles to better align with observed conditions.