ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion

• URL: http://arxiv.org/abs/2401.02993v2
• Date: Mon, 27 May 2024 07:04:19 GMT
• Title: ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion
• Authors: Shangyu Wu, Ying Xiong, Yufei Cui, Xue Liu, Buzhou Tang, Tei-Wei Kuo, Chun Jason Xue,
• Abstract summary: Retrieval-based augmentations (RA) incorporating knowledge from an external database into language models have greatly succeeded in various knowledge-intensive (KI) tasks. Existing works focus on concatenating retrievals with inputs to improve model performance. This paper proposes a new paradigm of RA named textbfReFusion, a computation-efficient Retrieval representation Fusion with bi-level optimization.
• Score: 22.164620956284466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-based augmentations (RA) incorporating knowledge from an external database into language models have greatly succeeded in various knowledge-intensive (KI) tasks. However, integrating retrievals in non-knowledge-intensive (NKI) tasks is still challenging. Existing works focus on concatenating retrievals with inputs to improve model performance. Unfortunately, the use of retrieval concatenation-based augmentations causes an increase in the input length, substantially raising the computational demands of attention mechanisms. This paper proposes a new paradigm of RA named \textbf{ReFusion}, a computation-efficient Retrieval representation Fusion with bi-level optimization. Unlike previous works, ReFusion directly fuses the retrieval representations into the hidden states of models. Specifically, ReFusion leverages an adaptive retrieval integrator to seek the optimal combination of the proposed ranking schemes across different model layers. Experimental results demonstrate that the proposed ReFusion can achieve superior and robust performance in various NKI tasks.

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