Generative AI Response Evaluation with Reference LLM Fine-Tuning
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Solution Overview
Problem
Existing generative AI tools lack effective methods for evaluating and fine-tuning their response quality, leading to inconsistencies and suboptimal performance in generating accurate and relevant outputs.
Innovation Solution
An evaluation system is implemented that utilizes multiple scoring models and a reference model to assess the outputs of generative AI tools, incorporating metrics like accuracy, recall, precision, and rank-aware metrics, with user feedback for refining the classification of outputs into 'good' or 'bad' datasets, and further evaluation by a generative pre-trained transformer model for indeterminate cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple scoring models and reference models are used to evaluate outputs, then measurement precision improves, but device complexity increases
Solution Approach 1:
The evaluation system is segmented into multiple independent scoring models (e.g., accuracy scorer, relevance scorer, grammar scorer) and a reference model. Each scoring model independently evaluates specific aspects of the AI tool outputs, allowing for specialized assessment without requiring a single monolithic complex system. This segmentation enables precise measurement of different dimensions while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The reference model serves multiple functions: it provides ground truth for comparison, acts as a benchmark for evaluating the AI tool outputs, and can be reused across different evaluation scenarios. The scoring models are designed to be universally applicable to various types of outputs (text, code, images) by evaluating different dimensions (accuracy, relevance, grammar), reducing the need for separate evaluation systems for each output type.
2Reliability
If user feedback is incorporated for refining classification, then reliability improves, but loss of time increases
Solution Approach 1:
The system performs preliminary automated evaluation using multiple scoring models and reference model comparisons before seeking user feedback. This preliminary action classifies outputs into categories (e.g., good/bad, accurate/inaccurate) based on automated assessments, allowing user feedback to be focused only on edge cases or indeterminate classifications rather than all outputs, thereby reducing overall time loss while maintaining reliability.
Solution Approach 2:
User feedback is systematically collected and integrated into the evaluation process to refine and improve classification reliability over time. The feedback mechanism allows users to correct automated classifications, provide additional context, or flag edge cases. This feedback loop continuously improves the system's reliability by learning from user corrections while maintaining efficient automated processing for routine cases.
3Productivity
If continuous learning and retraining are implemented, then productivity improves, but use of energy increases
Solution Approach 1:
The system implements periodic retraining and continuous learning at strategically determined intervals rather than continuously. Retraining is triggered by specific conditions such as accumulating a threshold number of user feedback examples, detecting performance degradation, or scheduling regular updates. This periodic approach maintains high response quality through continuous improvement while avoiding the constant energy consumption of uninterrupted training processes.
Solution Approach 2:
The system selectively discards outdated or low-quality training data and recovers only the most valuable examples for retraining. User feedback and high-quality examples are preserved and reused across training iterations, reducing the need to process entire datasets repeatedly. This selective approach maintains productivity improvements while minimizing the energy required for each retraining cycle by focusing computational resources on the most impactful data subsets.
Data Source
AI summary
Systems and methods of evaluating and fine-tuning a generative AI tool on a communication platform. The communication platform accesses a dataset comprising a user query and a response generated by an AI-based query system. The communication platform evaluates the response with respect to the user query using multiple AI-based scoring models to obtain multiple evaluation results. In response to determining that the multiple evaluation results are inconsistent, the communication platform evaluates the response with respect to the user query using a reference large language model (LLM) to provide a reference evaluation result. In response to determining that the reference evaluation result is decisive, the communication platform classifies, based on the reference evaluation result, the dataset to a data category of one or more data categories. The communication platform fine-tunes the AI-based query system based on a group of datasets in the data category.