Using Uncertainty

On dataset splits where generations are enabled (e.g. the Test split), you'll be seeing Uncertainty Scores and Token-level Uncertainty highlighting.

Uncertainty 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. At the token level:

  • Low Uncertainty means the model is fairly confident about what to say next, given the preceding tokens

  • High Uncertainty means the model is unsure what to say next, 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.

Some types of LLM hallucinations – particularly made-up names, citations, and URLs – are strongly correlated with Uncertainty. Monitoring Uncertainty can help you pinpoint these types of errors.

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