Resona: Improving Context Copying in Linear Recurrence Models with Retrieval

• URL: http://arxiv.org/abs/2503.22913v1
• Date: Fri, 28 Mar 2025 23:43:33 GMT
• Title: Resona: Improving Context Copying in Linear Recurrence Models with Retrieval
• Authors: Xinyu Wang, Linrui Ma, Jerry Huang, Peng Lu, Prasanna Parthasarathi, Xiao-Wen Chang, Boxing Chen, Yufei Cui,
• Abstract summary: We introduce __Resona__, a simple and scalable framework for augmenting linear recurrent models with retrieval.<n>Experiments on a variety of linear recurrent models demonstrate significant performance gains on a variety of synthetic as well as real-world natural language tasks.
• Score: 24.84741364872597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent shifts in the space of large language model (LLM) research have shown an increasing focus on novel architectures to compete with prototypical Transformer-based models that have long dominated this space. Linear recurrent models have proven to be a viable competitor due to their computational efficiency. However, such models still demonstrate a sizable gap compared to Transformers in terms of in-context learning among other tasks that require recalling information from a context. In this work, we introduce __Resona__, a simple and scalable framework for augmenting linear recurrent models with retrieval. __Resona__~augments models with the ability to integrate retrieved information from the provided input context, enabling tailored behavior to diverse task requirements. Experiments on a variety of linear recurrent models demonstrate that __Resona__-augmented models observe significant performance gains on a variety of synthetic as well as real-world natural language tasks, highlighting its ability to act as a general purpose method to improve the in-context learning and language modeling abilities of linear recurrent LLMs.

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