Yep, that’s the core of it! For example if I use the prompt “Paris is the capitol of France” and then I highlight “of” in my prompt, the layer predictions tab will show you what the model believes the next token to be through each layer.
You can watch the model start it’s guess in the very first layer (usually with something completely irrelevant) and then as it progress through each layer the model gets closer and closer until it converges on “France” as the most likely correct next token based on the context leading up to the selected token “of.” So then the model basically interprets it as “Paris is the capital of -> ? -> France”
You can see in the 1st layer the model was thinking “Paris is the capital of ales” then a deeper layer it was thinking “Paris is the capital of guardians” before it finally ended in the last layer with the correct prediction (remember, based on Paris is the capital of) “France”
The entropy tab calculates a few different metrics that also give a token-level and prompt-level hallucination risk assessment so you can see which types are higher risk for inducing hallucination in that particular model.
