Evaluating performance of LLM-based Applications

Large Language Models have revolutionized various applications across industries, but their performance needs rigorous evaluation to meet practical requirements of accuracy, efficiency, scalability, and ethics. A broad set of metrics and methods are needed to measure the performance of LLM-based applications, balancing technical performance with user experience and business needs. LLMs are non-trivial to evaluate due to their black box nature and multiplicity of downstream use cases, requiring a multifaceted performance measurement. There are four key dimensions of LLM performance: accuracy, cost, latency, and responsible AI. Accuracy depends on the actual use case, such as classification, text generation, or retrieval augmented generation, and can be measured using metrics like precision, recall, F1-score, BLEU, ROUGE, and METEOR scores. Latency and throughput determine the end usability of an application, and can be improved by horizontal or vertical scaling, but may depend on the overall application architecture and choice of LLM. Cost includes infrastructure costs, team and personnel costs, and other costs like data acquisition and management, and can vary based on deployment, scale, and architecture. Responsible AI metrics include fairness and bias, toxicity, explainability, hallucinations, and privacy. While these metrics are essential, they may not be enough to capture the context or unique user preferences, and human evaluation is necessary to complement these metrics.