Large Language Models (LLMs) have significantly impacted various functions in the field of Natural Language Processing (NLP), such as language translation, text summarization, and sentiment analysis. Despite these advantages, monitoring LLM’s performance and behavior has become increasingly challenging due to their growing size and complexity. Therefore, it is necessary to implement an effective and scalable architecture for monitoring these models, which would help quickly identify and address anomalous behavior or issues.
For online LLM monitoring, it is suggested that a modular architecture be implemented where each module can process model inference data and generate its own metrics. These metrics can then be sent to CloudWatch, which aggregates the data and sends notifications based on specific conditions. It is also important to choose the right metrics to track while implementing monitoring.
One of the metrics suggested is the semantic similarity between the prompt and completion. This can be done by transforming them into embeddings using an embedding model like Amazon Titan and then taking the cosine distance between these vectors.
Another monitoring aspect involves sentiment and toxicity. Detecting shifts in these areas helps ensure the model is functioning as expected. If there is an increase in sentiment or toxicity, it can be considered a red flag. Amazon Comprehend can assist in the detection of both these factors.
One more important issue to monitor is the ratio of refusals. This refers to the frequency with which an LLM declines completion due to lack of information. It can be a signal that the model has become overly sensitive and cautious if there is an increase in these refusals.
Overall, maintaining LLM observability is crucial for ensuring their reliable and trustworthy usage. By diligently monitoring these models, you can significantly reduce the risks associated with AI models, ensuring they deliver the value users are looking for.
Enabling successful monitoring of LLMs is feasible by utilizing AWS services like Amazon CloudWatch and AWS Lambda. By using these services, one can achieve a clear, real-time view of an LLM’s behavior, allowing an immediate response to any issues or anomalies.
Authors Bruno Klein and Rushabh Lokhande, who are both Senior Machine Learning Engineers, provide advice on implementing big data, machine learning and analytic solutions. They recommend exploring how to evaluate foundation models using SageMaker Clarify and also recommended other resources available on their GitHub repository.
