Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion

Retrieval-based augmentations that aim to incorporate knowledge from an external database into language models have achieved great success in various knowledge-intensive (KI) tasks, such as question-answering and text generation.However, integrating retrievals in non-knowledge-intensive (NKI) tasks, such as text classification, is still challenging.Existing works focus on concatenating retrievals to inputs as context to form the prompt-based inputs. Unfortunately, such methods require language models to have the capability to handle long texts.Besides, inferring such concatenated data would also consume a significant amount of computational resources.To solve these challenges, we propose \textbf{ReFusion} in this paper, a computation-efficient \textbf{Re}trieval representation \textbf{Fusion} with neural architecture search. The main idea is to directly fuse the retrieval representations into the language models.Specifically, ReFusion first retrieves the representations of similar sentences and uses Neural Architecture Search (NAS) to seek the optimal fusion structures. Experimental results demonstrate our ReFusion can achieve superior and robust performance on various NKI tasks.