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2023

Semantic Feature Verification in FLAN-T5

Suresh, Siddharth, Mukherjee, Kushin, Rogers, Timothy T.

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This study evaluates the potential of a large language model for aiding in generation of semantic feature norms - a critical tool for evaluating conceptual structure in cognitive science.

Building from an existing human-generated dataset, we show that machine-verified norms capture aspects of conceptual structure beyond what is expressed in human norms alone, and better explain human judgments of semantic similarity amongst items that are distally related.
The results suggest that LLMs can greatly enhance traditional methods of semantic feature norm verification, with implications for our understanding of conceptual representation in humans and machines.

Semantic Feature Verification in FLAN-T5 · Around

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On the internal structure of perceptual and semantic categories
Eleanor H Rosch · 1973
Earlier work this paper cites.
Calculation of signal detection theory measures
Harold Stanislaw and Natasha Todorov · 1999
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Semantic feature production norms for a large set of living and nonliving things
Ken McRae, George S Cree, Mark S Seidenberg, and Chris McNorgan · 2005
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