Scaling Retrieval-Based Language Models with a Trillion-Token Datastore
Scaling laws with respect to the amount of training data and the number of
parameters allow us to predict the cost-benefit trade-offs of pretraining
language models (LMs) in different configurations. In this paper, we consider
another dimension of scaling: the amount of data available at inference time.
Specifically, we find that increasing the size of the datastore used by a
retrieval-based LM monotonically improves language modeling and several
downstream tasks without obvious saturation, such that a smaller model
augmented with a large datastore outperforms a larger LM-only model on
knowledge-intensive tasks. By plotting compute-optimal scaling curves with
varied datastore, model, and pretraining data sizes, we show that using larger
datastores can significantly improve model performance for the same training
compute budget. We carry out our study by constructing a 1.4 trillion-token
datastore named MassiveDS, which is the largest and the most diverse
open-sourced datastore for retrieval-based LMs to date, and designing an
efficient pipeline for studying datastore scaling in a computationally
accessible manner. Finally, we analyze the effect of improving the retriever,
datastore quality filtering, and other design choices on our observed scaling
trends. Overall, our results show that datastore size should be considered as
an integral part of LM efficiency and performance trade-offs. To facilitate
future research, we open-source our datastore and code at
https://github.com/RulinShao/retrieval-scaling.
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Rulin Shao
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2024-07-22T07:10:33.164Z
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Rulin Shao
user
rulins
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Jacqueline He
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Akari Asai
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2024-07-19T11:23:20.147Z
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Akari Asai
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akariasai
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Weijia Shi
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Tim Dettmers
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Sewon Min
claimed_verified
2025-07-07T11:27:40.376Z
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Sewon Min
user
sewon
6699bf4e9fad95dc8e35118a
Luke Zettlemoyer
6699bf4e9fad95dc8e35118b
Pang Wei Koh
Scaling Retrieval-Based Language Models with a Trillion-Token Datastore
Scaling laws with respect to the amount of training data and the number of
parameters allow us to predict the cost-benefit trade-offs of pretraining
language models (LMs) in different configurations. In this paper, we consider
another dimension of scaling: the amount of data available at inference time.
Specifically, we find that increasing the size of the datastore used by a
retrieval-based LM monotonically improves language modeling and several
downstream tasks without obvious saturation, such that a smaller model
augmented with a large datastore outperforms a larger LM-only model on
knowledge-intensive tasks. By plotting compute-optimal scaling curves with
varied datastore, model, and pretraining data sizes, we show that using larger
datastores can significantly improve model performance for the same training
compute budget. We carry out our study by constructing a 1.4 trillion-token
datastore named MassiveDS, which is the largest and the most diverse
open-sourced datastore for retrieval-based LMs to date, and designing an
efficient pipeline for studying datastore scaling in a computationally
accessible manner. Finally, we analyze the effect of improving the retriever,
datastore quality filtering, and other design choices on our observed scaling
trends. Overall, our results show that datastore size should be considered as
an integral part of LM efficiency and performance trade-offs. To facilitate
future research, we open-source our datastore and code at
https://github.com/RulinShao/retrieval-scaling.
Increasing the size of the datastore for retrieval-based language models improves performance without obvious saturation, suggesting datastore size is a key factor in model efficiency and performance.
retrieval-based LMs datastore language modeling downstream tasks compute-optimal scaling curves pretraining MassiveDS retriever datastore quality filtering