I Don't Know: Explicit Modeling of Uncertainty with an [IDK] Token
Authors: name: Roi Cohen, name: Konstantin Dobler, name: Eden Biran, name: Gerard de Melo

Large Language Models are known to capture real-world knowledge, allowing
them to excel in many downstream tasks. Despite recent advances, these models
are still prone to what are commonly known as hallucinations, causing them to
emit unwanted and factually incorrect text. In this work, we propose a novel
calibration method that can be used to combat hallucinations. We add a special
[IDK] ("I don't know") token to the model's vocabulary and introduce an
objective function that shifts probability mass to the [IDK] token for
incorrect predictions. This approach allows the model to express uncertainty in
its output explicitly. We evaluate our proposed method across multiple model
architectures and factual downstream tasks. We find that models trained with
our method are able to express uncertainty in places where they would
previously make mistakes while suffering only a small loss of encoded
knowledge. We further perform extensive ablation studies of multiple variations
of our approach and provide a detailed analysis of the precision-recall
tradeoff of our method.