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.

通过使用预训练模型的潜在嵌入，我们提供了一种减少幻觉的方法。

利用认知神经网络减少 LLM 幻觉

Reducing LLM Hallucinations using Epistemic Neural Networks

Thompson sampling (TS) is a popular heuristic for action selection, but it
requires sampling from a posterior distribution. Unfortunately, this can become
computationally intractable in complex environments, such as those modeled
using neural networks. Approximate posterior samples can produce effective
actions, but only if they reasonably approximate joint predictive distributions
of outputs across inputs. Notably, accuracy of marginal predictive
distributions does not suffice. Epistemic neural networks (ENNs) are designed
to produce accurate joint predictive distributions. We compare a range of ENNs
through computational experiments that assess their performance in
approximating TS across bandit and reinforcement learning environments. The
results indicate that ENNs serve this purpose well and illustrate how the
quality of joint predictive distributions drives performance. Further, we
demonstrate that the \textit{epinet} -- a small additive network that estimates
uncertainty -- matches the performance of large ensembles at orders of
magnitude lower computational cost. This enables effective application of TS
with computation that scales gracefully to complex environments.

本文提出了一种名为 Epistemic 神经网络 (ENN) 的方法，通过准确的联合预测分布来近似输出预测分布来实现 TS，实验结果表明，ENN 可以相对准确地近似 TS，并且 	extit {epinet} 可以用更低的计算成本与大型集合的性能相匹配。