AI Agent Rubric Self-Evaluation for Hallucination Correction
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Solution Overview
Problem
Current AI generative systems lack self-critical abilities, leading to inaccurate, incoherent, or hallucinated responses, which can cause embarrassment, loss of trust, and legal or professional issues, and require laborious manual prompt refinement by users.
Innovation Solution
Implement a self-learning, self-evaluation, and self-correction mechanism using a rubric generated from both LLM and human agent feedback, with iterative calibration to achieve a confidence threshold, enabling the AI agent to assess and improve its responses and workflows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LLMs are trained with massive amounts of data to provide responses, then the LLMs can generate coherent and contextually relevant text, but the LLMs lack self-critical ability and may produce inaccurate or hallucinated responses
Solution Approach 1:
The patent implements a self-evaluation mechanism where the LLM generates multiple candidate responses and then evaluates them against a rubric to select the best response. This feedback loop allows the system to improve response accuracy by critically assessing its own outputs, directly addressing the lack of self-critical ability while maintaining the coherence benefits of extensive training data.
Solution Approach 2:
The system dynamically generates multiple candidate responses and iteratively evaluates them using a rubric-based scoring mechanism. This dynamic approach transforms the static LLM output process into an iterative refinement cycle, enabling the system to adapt and select the most accurate response while maintaining coherence.
2Measurement precision
If users manually refine prompts to improve response quality, then better results may be achieved, but the process is laborious and cumbersome requiring human trial and error
Solution Approach 1:
The patent enables the LLM to perform self-refinement by automatically generating multiple candidate responses and evaluating them against a rubric without human intervention. This self-service mechanism eliminates the need for laborious manual prompt refinement while achieving high response quality, directly addressing the time loss issue.
Solution Approach 2:
The system performs preliminary generation of multiple candidate responses before final selection, allowing the evaluation and refinement process to occur automatically before the user receives the final output. This preliminary action eliminates the need for users to engage in trial-and-error prompt refinement.
3Productivity
If LLMs provide responses without self-evaluation, then the system operates simply and quickly, but inaccurate or hallucinated responses cause embarrassment and loss of trust
Solution Approach 1:
The patent applies partial self-evaluation by generating multiple candidate responses (excessive action) and then evaluating them against a rubric to select the best one. This approach maintains relatively high productivity while improving reliability, as the system only performs evaluation on generated candidates rather than requiring extensive manual verification.
Data Source
AI summary
Systems and methods for an artificial intelligence (AI) agent to perform self-learning, self-evaluation based on a rubric and then perform self-correction as needed is described. The methods generate a rubric. The rubric includes the AI agent's performance data and evaluations of the data and related feedback by a separate LLM and a human agent. Once a level of confidence is achieved that the AI agent is performing at a threshold confidence level of the human agent, or that the separate LLM is evaluating the AI agent's performance within a threshold confidence of the human agent's evaluation of the same, the rubric in which the AI agent's performance and evaluation data is inputted is determined to be complete for use in a self-evaluation. The AI agent may then use the rubric to self-evaluate and self-correct its performance without a need for human evaluation.