  Reducing and detecting hallucinations in large language models is an open
research problem. In this project, we attempt to leverage recent advances in
the field of uncertainty estimation to reduce hallucinations in frozen large
language models. Epistemic neural networks have recently been proposed to
improve output joint distributions for large pre-trained models. ENNs are small
networks attached to large, frozen models to improve the model's joint
distributions and uncertainty estimates. In this work, we train an epistemic
neural network on top of the Llama-2 7B model combined with a contrastive
decoding feature enhancement technique. We are the first to train an ENN for
the next token prediction task and explore the efficacy of this method in
reducing hallucinations on the TruthfulQA dataset. In essence, we provide a
method that leverages a pre-trained model's latent embeddings to reduce
hallucinations.
