New System Proposed to Monitor Large Language Models for Reliability and Safety
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Proposes a modular, customizable architecture using AWS services to monitor large language models (LLMs) in real-time. Metrics compute modules process prompts and responses and output metrics to CloudWatch.
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Suggests tracking semantic similarity between prompts and responses, sentiment, toxicity, and ratio of model refusals to monitor model performance and integrity.
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Provides examples of specific metric compute modules for tracking semantic similarity, sentiment/toxicity analysis, and refusal ratios.
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Discusses the importance of monitoring LLMs to mitigate risks and ensure reliable, trustworthy performance.
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Recommends exploring SageMaker Clarify and MLOps services to evaluate and operationalize monitoring of foundation models like LLMs at scale.