  Large language models (LLMs) have achieved remarkable progress in the field
of natural language processing (NLP), demonstrating remarkable abilities in
producing text that resembles human language for various tasks. This opens up
new opportunities for employing them in recommender systems (RSs). In this
paper, we specifically examine the sample efficiency of LLM-enhanced
recommender systems, which pertains to the model's capacity to attain superior
performance with a limited quantity of training data. Conventional
recommendation models (CRMs) often need a large amount of training data because
of the sparsity of features and interactions. Hence, we propose and verify our
core viewpoint: Large Language Models Make Sample-Efficient Recommender
Systems. We propose a simple yet effective framework (i.e., Laser) to validate
the viewpoint from two aspects: (1) LLMs themselves are sample-efficient
recommenders; and (2) LLMs, as feature generators and encoders, make CRMs more
sample-efficient. Extensive experiments on two public datasets show that Laser
requires only a small fraction of training samples to match or even surpass
CRMs that are trained on the entire training set, demonstrating superior sample
efficiency.
