Retrieval-augmented language models show promise in addressing issues like
outdated information and hallucinations in language models (LMs). However,
current research faces two main problems: 1) determining what information to
retrieve, and 2) effectively combining retrieved information during generation.
We argue that valuable retrieved information should not only be related to the
current source text but also consider the future target text, given the nature
of LMs that model future tokens. Moreover, we propose that aggregation using
latent variables derived from a compact latent space is more efficient than
utilizing explicit raw text, which is limited by context length and susceptible
to noise. Therefore, we introduce RegaVAE, a retrieval-augmented language model
built upon the variational auto-encoder (VAE). It encodes the text corpus into
a latent space, capturing current and future information from both source and
target text. Additionally, we leverage the VAE to initialize the latent space
and adopt the probabilistic form of the retrieval generation paradigm by
expanding the Gaussian prior distribution into a Gaussian mixture distribution.
Theoretical analysis provides an optimizable upper bound for RegaVAE.
Experimental results on various datasets demonstrate significant improvements
in text generation quality and hallucination removal.

检索增强语言模型通过引入隐变量聚合检索信息解决了过时信息和虚构问题，并以 RegaVAE 作为一个例子，展示了在文本生成质量和幻觉去除方面的显著改进。