In Depth Analysis: Key Performance Indicators for Validating Large Language Model

Today’s post shares a summary of key performance indicators (KPIs) for validating large language models (LLMs). It offers concise explanations and formulas for each KPI, along with example values to illustrate what constitutes “good” and “poor” model performance. The KPIs cover various aspects of model performance, including predictive accuracy, text quality, fairness, and efficiency.

Perplexity: Evaluates how well a language model predicts a sequence of words. Lower values indicate better performance.

BLEU Score (Bilingual Evaluation Understudy): Measures the quality of machine-generated text compared to a reference text. Higher scores indicate better quality and closer alignment with reference texts.

ROUGE Score (Recall-Oriented Understudy for Gisting Evaluation): Evaluates the overlap of n-grams between generated and reference texts, focusing on recall. Higher scores indicate better text quality and alignment with reference texts.

Precision: Measures the proportion of relevant instances among the retrieved instances. Higher values indicate fewer false positives.

Recall: Measures the proportion of relevant instances that were retrieved. Higher values indicate fewer false negatives.

F1 Score: Provides a single measure of performance by balancing precision and recall. Higher scores indicate better overall performance.

Latency: Measures the time taken by the model to generate a response. Lower values indicate faster responses.

Throughput: Measures the number of requests the model can handle in a given period. Higher values indicate better efficiency.

Memory Usage: Indicates how much memory the model consumes during operation. Lower memory usage is preferable for scalability and efficiency.

Equalized Odds: Measures the difference in true positive and false positive rates across different demographic groups. Lower values indicate fairer models with less bias.

Demographic Parity: Checks if different groups receive positive outcomes at the same rate. Lower values indicate less bias.

Calibration: Measures the alignment of predicted probabilities with true probabilities. Lower values indicate better calibration.