Impact of Retrieval Store Size Ratio on AmbiEval Accuracy 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

How does varying the ratio of retrieval store size to pretraining corpus size impact accuracy on the AmbiEval benchmark under a fixed total data budget?

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