Interactive token sampling, autoregressive cascade modeling & mitigation lab
Ranked logits after Softmax(zi/T) with nucleus mask applied:
Once an erroneous token is committed, it enters the frozen prefix. The model conditions on its own fictional premise, compounding error probability exponentially.
Parametric weights store smooth statistical associations rather than isolated tabular facts. High entropy creates smooth interpolation across semantically similar yet false entities.
RLHF models heavily weight conversational agreeableness. User confirmation bias in prompts acts as an adversarial prior, suppressing factual tokens.