Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models

Amirhossein Kazemnejad, Mehdi Rezagholizadeh, Prasanna Parthasarathi, Sarath Chandar

Abstract
While pre-trained language models (PLMs) have shown evidence of acquiring vast amounts of knowledge, it remains unclear how much of this parametric knowledge is actually usable in performing downstream tasks. We propose a systematic framework to measure parametric knowledge utilization in PLMs. Our framework first extracts knowledge from a PLM’s parameters and subsequently constructs a downstream task around this extracted knowledge. Performance on this task thus depends exclusively on utilizing the model’s possessed knowledge, avoiding confounding factors like insufficient signal. As an instantiation, we study factual knowledge of PLMs and measure utilization across 125M to 13B parameter PLMs. We observe that: (1) PLMs exhibit two gaps - in acquired vs. utilized knowledge, (2) they show limited robustness in utilizing knowledge under distribution shifts, and (3) larger models close the acquired knowledge gap but the utilized knowledge gap remains. Overall, our study provides insights into PLMs’ capabilities beyond their acquired knowledge.
Anthology ID:
2023.findings-emnlp.285
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4305–4319
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.285
DOI:
10.18653/v1/2023.findings-emnlp.285
Bibkey:
Cite (ACL):
Amirhossein Kazemnejad, Mehdi Rezagholizadeh, Prasanna Parthasarathi, and Sarath Chandar. 2023. Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4305–4319, Singapore. Association for Computational Linguistics.
Cite (Informal):
Measuring the Knowledge Acquisition-Utilization Gap in Pretrained Language Models (Kazemnejad et al., Findings 2023)
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