COPEN: Probing Conceptual Knowledge in Pre-trained Language Models
This addresses a critical bottleneck for achieving human-like cognition in PLMs, though it is incremental as it builds on existing knowledge probing methods by extending them to conceptual knowledge.
The authors tackled the problem of evaluating conceptual knowledge in pre-trained language models (PLMs), which had been overlooked in prior work focused on factual knowledge, and found that existing PLMs systematically lack conceptual knowledge and suffer from spurious correlations, as shown through experiments on 24k data instances covering 393 concepts.
Conceptual knowledge is fundamental to human cognition and knowledge bases. However, existing knowledge probing works only focus on evaluating factual knowledge of pre-trained language models (PLMs) and ignore conceptual knowledge. Since conceptual knowledge often appears as implicit commonsense behind texts, designing probes for conceptual knowledge is hard. Inspired by knowledge representation schemata, we comprehensively evaluate conceptual knowledge of PLMs by designing three tasks to probe whether PLMs organize entities by conceptual similarities, learn conceptual properties, and conceptualize entities in contexts, respectively. For the tasks, we collect and annotate 24k data instances covering 393 concepts, which is COPEN, a COnceptual knowledge Probing bENchmark. Extensive experiments on different sizes and types of PLMs show that existing PLMs systematically lack conceptual knowledge and suffer from various spurious correlations. We believe this is a critical bottleneck for realizing human-like cognition in PLMs. COPEN and our codes are publicly released at https://github.com/THU-KEG/COPEN.