Impact of Retrieval Store Size Relative to Pretraining Corpus on Multimodal LATEX Benchmark Performance Under Fixed Data Budget

Abstract

Abstract: Retrieval-augmented generation (RAG) improves language model (LM) performance by providing relevant context at test time for knowledge-intensive situations. However, the relationship between parametric knowledge acquired during pretraining and non-parametric knowledge accessed via retrieval remains poorly understood, especially under fixed data budgets. In this work, we systematically study the trade-off between pretraining corpus size and retrieval store size across a wide range of model and data scales. We train OLMo-2-based LMs ranging from 30M to 3B parameters on up to 100B tokens of DCLM

Research Question

What is the impact of retrieval store size relative to pretraining corpus size on the performance of multimodal models on the LATEX benchmarks under a fixed data budget?

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