Definition: Measures how much the model is deciding randomly between multiple ways of continuing the output. Uncertainty is measured at both the token level and the response level. Higher uncertainty means the model is less certain. Availability: Uncertainty can be calculated only with the LLM intergrations that provide log probabilities. Those are:
  • OpenAI:
    • Any Evaluate runs created from the Galileo Playground or with pq.run(...), using the chosen model.
    • Any Evaluate workflow runs using davinci-001.
    • Any Observe worfklows using davinci-001.
  • Azure OpenAI:
    • Any Evaluate runs created from the Galileo Playground or with pq.run(...), using the chosen model.
    • Any Evaluate workflow runs text-davinci-003 or text-curie-001, if they’re available in your Azure deployment.
    • Any Observe worfklows using text-davinci-003 or text-curie-001, if they’re available in your Azure deployment.
Calculation: Uncertainty at the token level tells us how confident the model is of the next token given the preceding tokens. Uncertainty at the response level is simply the maximum token-level Uncertainty, over all the tokens in the model’s response. It is calculated using OpenAI’s Davinci models or Chat Completion models (available via OpenAI or Azure).
To calculate the Uncertainty metric, we require havingtext-curie-001 or text-davinci-003models available in your Azure environment. This is required in order to fetch log probabilities. For Galileo’s Guardrail metrics that rely on GPT calls (Factuality and Groundedness), we require using 0613 or above versions of gpt-35-turbo (Azure docs).
What to do when uncertainty is low? Our research has found high uncertainty scores correlate with hallucinations, made up facts, and citations. Looking at highly uncertain responses can flag areas where your model is struggling.