Collateral facilitation in humans and language models

James Michaelov, Benjamin Bergen


Abstract
Are the predictions of humans and language models affected by similar things? Research suggests that while comprehending language, humans make predictions about upcoming words, with more predictable words being processed more easily. However, evidence also shows that humans display a similar processing advantage for highly anomalous words when these words are semantically related to the preceding context or to the most probable continuation. Using stimuli from 3 psycholinguistic experiments, we find that this is also almost always also the case for 8 contemporary transformer language models (BERT, ALBERT, RoBERTa, XLM-R, GPT-2, GPT-Neo, GPT-J, and XGLM). We then discuss the implications of this phenomenon for our understanding of both human language comprehension and the predictions made by language models.
Anthology ID:
2022.conll-1.2
Volume:
Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Hybrid)
Venue:
CoNLL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
13–26
Language:
URL:
https://aclanthology.org/2022.conll-1.2
DOI:
Bibkey:
Cite (ACL):
James Michaelov and Benjamin Bergen. 2022. Collateral facilitation in humans and language models. In Proceedings of the 26th Conference on Computational Natural Language Learning (CoNLL), pages 13–26, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
Cite (Informal):
Collateral facilitation in humans and language models (Michaelov & Bergen, CoNLL 2022)
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PDF:
https://preview.aclanthology.org/author-url/2022.conll-1.2.pdf