ArticleScientific reports2023
Overlap in meaning is a stronger predictor of semantic activation in GPT-3 than in humans.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 48 citations in OpenAlex.
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- Title and abstract screening for literature reviews using large language models: an exploratory study in the biomedical domain.Systematic reviews · 2024Article
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- Large language models know how the personality of public figures is perceived by the general public.Scientific reports · 2024Article
- Exploring Capabilities of Large Language Models such as ChatGPT in Radiation Oncology.Advances in radiation oncology · 2024Article
- Machine vs. human, who makes a better judgment on innovation? Take GPT-4 for example.Frontiers in artificial intelligence · 2023Article
- Adaptive Cancer Therapy in the Age of Generative Artificial Intelligence.Cancer control : journal of the Moffitt Cancer CenterReview
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2 authors at 2 institutions in 3 countries.
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Abstract
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.
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