Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying solely on their parameters to encode a wealth of world knowledge. This paper aims to understand LMs' strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments of 10 models and 4 augmentation methods on PopQA, our new open-domain QA dataset with 14k questions. We find that LMs struggle with less popular factual knowledge, and that scaling fails to appreciably improve memorization of factual knowledge in the long tail. We then show that retrieval-augmented LMs largely outperform orders of magnitude larger LMs, while unassisted LMs remain competitive in questions about high-popularity entities. Based on those findings, we devise a simple, yet effective, method for powerful and efficient retrieval-augmented LMs, which retrieves non-parametric memories only when necessary. Experimental results show that this significantly improves models' performance while reducing the inference costs.

翻译：尽管大型语言模型（LM）在各种任务上表现出色，但在需要丰富世界知识的任务中仍面临挑战，这说明仅依赖其参数编码海量世界知识存在局限性。本文旨在通过大规模知识探测实验——在包含1.4万个问题的开放域问答数据集PopQA上对10种模型和4种增强方法进行测试——来理解LM在事实知识记忆方面的优势与局限。我们发现LM难以记忆小众事实知识，且模型规模扩展未能显著改善长尾事实知识的记忆效果。进一步研究表明，检索增强型LM大幅优于参数量级更大的模型，而未经辅助的LM在回答高知名度实体相关问题时仍具竞争力。基于这些发现，我们设计了一种简单而有效的方法来构建强大高效的检索增强型LM：仅在必要时检索非参数化记忆。实验结果表明，该方法在显著提升模型性能的同时降低了推理成本。