RAVEN: In-Context Learning with Retrieval Augmented Encoder-Decoder
  Language Models
Authors: name: Jie Huang, name: Wei Ping, name: Peng Xu, name: Mohammad Shoeybi, name: Kevin Chen-Chuan Chang, name: Bryan Catanzaro

In this paper, we investigate the in-context learning ability of
retrieval-augmented encoder-decoder language models. We first conduct a
comprehensive analysis of the state-of-the-art ATLAS model and identify its
limitations in in-context learning, primarily due to a mismatch between
pretraining and testing, as well as a restricted context length. To address
these issues, we propose RAVEN, a model that combines retrieval-augmented
masked language modeling and prefix language modeling. We further introduce
Fusion-in-Context Learning to enhance the few-shot performance by enabling the
model to leverage more in-context examples without requiring additional
training or model modifications. Through extensive experiments, we demonstrate
that RAVEN significantly outperforms ATLAS and achieves results comparable to
the most advanced language models in certain scenarios, despite having
substantially fewer parameters. Our work underscores the potential of
retrieval-augmented encoder-decoder language models for in-context learning and
encourages further research in this direction.