Context Variance Evaluation of Pretrained Language Models for Prompt-based Biomedical Knowledge Probing

• URL: http://arxiv.org/abs/2211.10265v1
• Date: Fri, 18 Nov 2022 14:44:09 GMT
• Title: Context Variance Evaluation of Pretrained Language Models for Prompt-based Biomedical Knowledge Probing
• Authors: Zonghai Yao, Yi Cao, Zhichao Yang, Hong Yu
• Abstract summary: We show that prompt-based probing methods can only probe a lower bound of knowledge. We introduce context variance into the prompt generation and propose a new rank-change-based evaluation metric.
• Score: 9.138354194112395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pretrained language models (PLMs) have motivated research on what kinds of knowledge these models learn. Fill-in-the-blanks problem (e.g., cloze tests) is a natural approach for gauging such knowledge. BioLAMA generates prompts for biomedical factual knowledge triples and uses the Top-k accuracy metric to evaluate different PLMs' knowledge. However, existing research has shown that such prompt-based knowledge probing methods can only probe a lower bound of knowledge. Many factors like prompt-based probing biases make the LAMA benchmark unreliable and unstable. This problem is more prominent in BioLAMA. The severe long-tailed distribution in vocabulary and large-N-M relation make the performance gap between LAMA and BioLAMA remain notable. To address these, we introduce context variance into the prompt generation and propose a new rank-change-based evaluation metric. Different from the previous known-unknown evaluation criteria, we propose the concept of "Misunderstand" in LAMA for the first time. Through experiments on 12 PLMs, our context variance prompts and Understand-Confuse-Misunderstand (UCM) metric makes BioLAMA more friendly to large-N-M relations and rare relations. We also conducted a set of control experiments to disentangle "understand" from just "read and copy".

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