7 Ways to Monitor Large Language Model Behavior

This article discusses seven ways to monitor the behavior of Large Language Models (LLMs) using LangKit and WhyLabs. The focus is on tracking the evolution of LLMs, such as ChatGPT, which have revolutionized Natural Language Processing with their ability to generate coherent human-like text. The article covers various metrics like ROUGE, bias, text quality, semantic similarity, regex patterns, refusals, and toxicity and sentiment analysis. It also provides a detailed example of how these metrics can be calculated for ChatGPT's responses over 35 days using the ELITE5 dataset. The monitoring process involves generating whylogs profiles for each day's data and uploading them to the WhyLabs observability platform. The article concludes by analyzing the changes in LLM behavior over time, highlighting improvements in various metrics after a significant upgrade on March 23.