In-Context Learning with Retrieval Augmented Encoder-Decoder Language Models
Keywords: Retrieval-Augmented Language Models, In-Context Learning, Open-Domain Question Answering
TL;DR: We investigate the in-context learning ability of retrieval-augmented encoder-decoder language models.
Abstract: 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.
Primary Area: representation learning for computer vision, audio, language, and other modalities
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Submission Number: 4165
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