Language models (LMs) pretrained on large corpora of text from the web have
been observed to contain large amounts of various types of knowledge about the
world. This observation has led to a new and exciting paradigm in knowledge
graph construction where, instead of manual curation or text mining, one
extracts knowledge from the parameters of an LM. Recently, it has been shown
that finetuning LMs on a set of factual knowledge makes them produce better
answers to queries from a different set, thus making finetuned LMs a good
candidate for knowledge extraction and, consequently, knowledge graph
construction. In this paper, we analyze finetuned LMs for factual knowledge
extraction. We show that along with its previously known positive effects,
finetuning also leads to a (potentially harmful) phenomenon which we call
Frequency Shock, where at the test time the model over-predicts rare entities
that appear in the training set and under-predicts common entities that do not
appear in the training set enough times. We show that Frequency Shock leads to
a degradation in the predictions of the model and beyond a point, the harm from
Frequency Shock can even outweigh the positive effects of finetuning, making
finetuning harmful overall. We then consider two solutions to remedy the
identified negative effect: 1- model mixing and 2- mixture finetuning with the
LM's pre-training task. The two solutions combined lead to significant
improvements compared to vanilla finetuning.

本文分析语言模型在事实知识提取中的应用，发现 finetuning 会造成一个负面现象 - Frequency Shock，导致模型预测能力下降，因此提出两种解决方案（模型混合和混合 finetuning），并验证实验表明这两种解决方案相比原始 finetuning 均获得了显著的改进。