RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models
This work addresses in-context learning challenges for NLP researchers, offering an incremental improvement through a novel method.
The paper tackled limitations in in-context learning for retrieval-augmented encoder-decoder language models by proposing RAVEN, which combines retrieval-augmented masked and prefix language modeling, achieving performance comparable to advanced models with fewer parameters.
In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models. We first conduct a comprehensive analysis of existing models and identify their limitations in in-context learning, primarily due to a mismatch between pretraining and inference, 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. Through extensive experiments, we demonstrate that our simple yet effective design significantly improves performance, achieving 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.