LLM Classification Evaluation Using Repeated Prompt Sampling
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Solution Overview
Problem
Generative models exhibit variability in classification tasks due to their probabilistic nature, leading to inconsistent outputs for the same input, especially when dealing with nuanced or ambiguous sentiment in text.
Innovation Solution
A system is developed to evaluate generative models by submitting the same prompt multiple times, calculating distance values between outputs and expected results, and computing an evaluation based on these distances, including confidence scores for instructions and models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative models are used for classification tasks, then the models can handle nuanced and ambiguous sentiment in text, but the output consistency deteriorates due to the probabilistic nature of the models
Solution Approach 1:
The system performs preliminary actions by submitting the same prompt multiple times before final evaluation to capture the probabilistic variability of generative models. This preliminary repeated execution allows the system to gather multiple output samples that reflect the model's inherent stochasticity, enabling more reliable assessment of both consistency and nuanced sentiment handling capabilities
Solution Approach 2:
The system implements feedback mechanisms by comparing multiple model outputs against each other and against expected results. The evaluation process uses distance calculations and confidence scores that feed back into the assessment of model performance, allowing the system to quantify both the consistency and the quality of nuanced sentiment classification across multiple trials
2Reliability
If multiple submissions of the same prompt are made to evaluate generative models, then the assessment reliability improves, but the time consumption increases
Solution Approach 1:
The system applies partial action by determining an optimal number of prompt submissions based on diminishing returns analysis. Rather than indefinitely increasing the number of submissions, the system identifies the point where additional submissions provide minimal improvement to assessment reliability, thus avoiding excessive time consumption while maintaining sufficient evaluation accuracy
Solution Approach 2:
The system substitutes mechanical repeated execution with computational efficiency by using distance calculations and confidence scoring mechanisms. Instead of simply counting identical outputs, the system employs mathematical metrics to assess similarity and reliability, reducing the time required for evaluation while maintaining or improving assessment accuracy
3Measurement precision
If distance values are calculated between outputs and expected results, then the measurement precision improves, but the computational complexity increases
Solution Approach 1:
The system changes parameters by selecting and optimizing the distance calculation metric based on the specific classification task and model behavior. Rather than using a fixed complex measurement approach, the system adapts the distance metric parameters to balance precision requirements with computational efficiency, choosing appropriate similarity measures that provide sufficient measurement accuracy without excessive computational overhead
Data Source
AI summary
Techniques for evaluating the efficacy of large language models on classification tasks are disclosed. A prompt that includes an instruction and a content item to be classified is submitted multiple times to a large language model. For each submission of the prompt, a corresponding classification label from a set of two or more classification labels is returned. Each classification label is compared to the expected classification label for the content item using a label distance value metric. Using the label distance value metric, a confidence score is generated.