LLM Metrics: Key Metrics Explained

Organizations that monitor their LLMs will benefit from higher performing models at higher efficiency, while meeting ethical considerations like ensuring privacy and eliminating bias and toxicity. In this blog post, we bring the top LLM metrics we recommend measuring and when to use each one. In the end, we explain how to implement these metrics in your ML and gen AI pipelines.

Why Do We Need to Monitor LLM Metrics?

Monitoring the metrics of LLMs enhances performance optimization, enables understanding user interaction and ensures ethical compliance. In more detail:

• Accuracy - Monitoring the outputs of the model is the main course for validating its reliability at a given task. It is the first flag that needs to be raised in case the model needs to go into another phase of development, whether it is prompt engineering the inputs to the model or fine-tuning the model itself.
• Resource Management - LLMs require significant computational resources. Metrics related to resource utilization help in managing these resources effectively and reduce operational costs.
• User Interaction - Monitoring metrics related to user interactions helps in understanding how users engage with the model. These insights can guide enhancements in user experience, making the model more intuitive and responsive to user needs.
• Ethical Compliance and Bias Reduction - Monitoring metrics related to the ethical use of LLMs ensures the trustworthiness of the model. This is important for preventing incomplete and incorrect responses, responses with the wrong tone, violation of privacy or ethical standards (like preventing ePHI leakage in healthcare), preventing sensitive business data leakage, and more.

Key LLM Metrics to Track

Here are the top metrics to take into consideration, including LLM performance metrics, LLM accuracy metrics, user interaction metrics, resource management metrics and ethical metrics:

Performance Optimization Metrics:

• Latency - How quickly an LLM can provide a response after receiving an input. Faster response times enhance user satisfaction and engagement.
• Throughput - The number of tasks or queries an LLM can handle within a given time frame, for assessing the model's capability to serve multiple requests simultaneously. This is important for scalability and performance in production environments.
• Resource Utilization (CPU/GPU Memory Usage) - How efficiently an LLM uses computational resources, such as CPU and GPU memory. Optimal resource utilization ensures that the model runs efficiently, enabling cost-effective scaling and sustainability in deployment.
• Data Drift - Data drift occurs when an LLM’s performance degrades over time due to changes in data patterns or user interactions. Monitoring for drift helps maintain the model's accuracy and relevance, requiring periodic updates or retraining.
• XMI/CXMI (Cross-Modal Information/Cross-Modal Mutual Information) - For evaluating the performance of models in tasks that involve multiple modalities (like text and images). They measure how effectively a model can understand and generate content that accurately bridges different forms of input.
• Sensibleness and Specificity - The relevance and appropriateness of LLM responses. Sensibleness ensures responses are reasonable within the context. Specificity measures how well responses are tailored to the specific input, avoiding generic or irrelevant content.

User Engagement Metrics

• Session Length - This metric measures the duration of interaction between a user and an LLM within a single session. Longer session lengths may indicate higher user satisfaction and engagement, but they could also reflect confusion or difficulty in obtaining the desired information.
• Token Efficiency - The effectiveness of an LLM in conveying information with fewer tokens (words or characters). Higher token efficiency can indicate a model’s ability to generate concise and relevant responses, optimizing computational resources.

Ethical Compliance Indicators

• The LLM’s adherence to ethical guidelines, including respect for privacy, non-discrimination, transparency and fairness. For example, ensuring no violence, toxicity, misuse (e.g, if a company builds a chatbot for answering questions about their insurance policy, but users are trying to use it to do homework), or prompt attacks that cause the model to response in a certain way or share sensitive information, This ensures that LLMs are used responsibly and do not perpetuate harm.

In addition, the following metrics should be developed/implemented independently and per use case and task to measure and monitor LLMs:

• Perplexity - How well a language model predicts a sample of text. Lower perplexity indicates better performance.
• BLEU (Bilingual Evaluation Understudy) - The similarity between the generated text and reference text. It's commonly used in machine translation tasks but can be applied more broadly to evaluate the quality of generated text.
• ROUGE (Recall-Oriented Understudy for Gisting Evaluation) - The quality of summaries produced by a model compared to reference summaries. It assesses the overlap of n-grams (sequences of n words) between the generated summary and the reference summaries.
• METEOR (Metric for Evaluation of Translation with Explicit Ordering) - The quality of machine translation by considering both precision and recall. It incorporates synonyms and stems into its evaluation, making it more robust than BLEU and ROUGE in some cases.

These metrics can also be customized:

• F1 Score - A measure of predictive performance, combining precision and recall into a single value. It's commonly used in natural language processing tasks like named entity recognition and sentiment analysis.
• Accuracy - The extent to which an LLM's responses match the expected or correct outcomes. High accuracy indicates a model's effectiveness in understanding and generating relevant content.

How to Implement LLM Monitoring with MLOps

Implementing LLM monitoring within an MLOps framework involves establishing a systematic approach to manage, deploy, monitor and update the models efficiently. Here’s how to integrate LLM monitoring within an MLOps pipeline:

1. Start by identifying the KPIs relevant to your LLM. Clearly defining these metrics helps in setting up effective monitoring.

2. Implement data and model versioning to track and manage changes over time. This can male it easier to roll back to previous versions if a new model version performs poorly or introduces bias.

3. Adapt CI/CD practices for ML workflows. Automate the testing of your LLM for performance, accuracy and bias as part of the CI process. Use CD to automate the deployment of updated models into production, ensuring that the deployment process is smooth and that the model version aligns with the data it was trained on.

4. Set up a robust monitoring infrastructure to track the defined metrics in real-time.

5. Collect user feedback, allowing to perform RLHF that will better suit the model to user expectations.

6. Implement alerting mechanisms for anomaly detection. If a metric deviates significantly from its normal range, an alert should be triggered. This can help in quickly identifying and mitigating issues like performance degradation, unexpected spikes in resource usage, or ethical concerns.

7. Based on the insights gained from monitoring, set up processes for periodic model retraining and updating. Automate the continuous fine-tuning process as much as possible, using pipelines that can trigger retraining based on specific criteria, such as data drift or degradation in model performance.

Implement LLM evaluation metrics in your pipelines today. Start now.