LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent
  Sentence Spaces
Yingji Zhang Danilo S. Carvalho Ian Pratt-Hartmann André Freitas
Deep generative neural networks, such as Variational AutoEncoders (VAEs),
offer an opportunity to better understand and control language models from the
perspective of sentence-level latent spaces. To combine the controllability of
VAE latent spaces with the state-of-the-art performance of recent large
language models (LLMs), we present in this work LlaMaVAE, which combines
expressive encoder and decoder models (sentenceT5 and LlaMA) with a VAE
architecture, aiming to provide better text generation control to LLMs. In
addition, to conditionally guide the VAE generation, we investigate a new
approach based on flow-based invertible neural networks (INNs) named Invertible
CVAE. Experimental results reveal that LlaMaVAE can outperform the previous
state-of-the-art VAE language model, Optimus, across various tasks, including
language modelling, semantic textual similarity and definition modelling.
Qualitative analysis on interpolation and traversal experiments also indicates
an increased degree of semantic clustering and geometric consistency, which
enables better generation control.