Overlap in meaning is a stronger predictor of semantic activation in GPT-3 than in humans.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
28 03 2023
28 03 2023
Historique:
received:
23
12
2022
accepted:
24
03
2023
medline:
30
3
2023
entrez:
28
3
2023
pubmed:
29
3
2023
Statut:
epublish
Résumé
Modern large language models generate texts that are virtually indistinguishable from those written by humans and achieve near-human performance in comprehension and reasoning tests. Yet, their complexity makes it difficult to explain and predict their functioning. We examined a state-of-the-art language model (GPT-3) using lexical decision tasks widely used to study the structure of semantic memory in humans. The results of four analyses showed that GPT-3's patterns of semantic activation are broadly similar to those observed in humans, showing significantly higher semantic activation in related (e.g., "lime-lemon") word pairs than in other-related (e.g., "sour-lemon") or unrelated (e.g., "tourist-lemon") word pairs. However, there are also significant differences between GPT-3 and humans. GPT-3's semantic activation is better predicted by similarity in words' meaning (i.e., semantic similarity) rather than their co-occurrence in the language (i.e., associative similarity). This suggests that GPT-3's semantic network is organized around word meaning rather than their co-occurrence in text.
Identifiants
pubmed: 36977744
doi: 10.1038/s41598-023-32248-6
pii: 10.1038/s41598-023-32248-6
pmc: PMC10050205
doi:
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5035Informations de copyright
© 2023. The Author(s).
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