BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?
This addresses the gap in understanding how language models capture abstract semantic relations, which is important for NLP researchers and developers, but it is incremental as it builds on existing models without introducing new methods.
The paper tackled the problem of whether pre-trained language models can identify analogies, an unsupervised task, and found that off-the-shelf models like GPT-2 and RoBERTa achieve some success but struggle with abstract relations and are sensitive to architecture, with BERT failing to outperform word embedding models.
Analogies play a central role in human commonsense reasoning. The ability to recognize analogies such as "eye is to seeing what ear is to hearing", sometimes referred to as analogical proportions, shape how we structure knowledge and understand language. Surprisingly, however, the task of identifying such analogies has not yet received much attention in the language model era. In this paper, we analyze the capabilities of transformer-based language models on this unsupervised task, using benchmarks obtained from educational settings, as well as more commonly used datasets. We find that off-the-shelf language models can identify analogies to a certain extent, but struggle with abstract and complex relations, and results are highly sensitive to model architecture and hyperparameters. Overall the best results were obtained with GPT-2 and RoBERTa, while configurations using BERT were not able to outperform word embedding models. Our results raise important questions for future work about how, and to what extent, pre-trained language models capture knowledge about abstract semantic relations.