Semantic Scholar Open Access 2022 683 sitasi

Using cognitive psychology to understand GPT-3

Marcel Binz Eric Schulz

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Abstrak

Significance Language models are trained to predict the next word for a given text. Recently, it has been shown that scaling up these models causes them to not only generate language but also to solve challenging reasoning problems. The present article lets a large language model (GPT-3) do experiments from the cognitive psychology literature. We find that GPT-3 can solve many of these tasks reasonably well, despite being only taught to predict future word occurrences on a vast amount of text from the Internet and books. We additionally utilize analysis tools from the cognitive psychology literature to demystify how GPT-3 solves different tasks and use the thereby acquired insights to make recommendations for how to improve future model iterations.

Topik & Kata Kunci

Computer Science Medicine

Penulis (2)

Marcel Binz

Eric Schulz

Format Sitasi

APA MLA BibTeX

Binz, M., Schulz, E. (2022). Using cognitive psychology to understand GPT-3. https://doi.org/10.1073/pnas.2218523120

Akses Cepat

Lihat di Sumber doi.org/10.1073/pnas.2218523120

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 683×
Sumber Database: Semantic Scholar
DOI: 10.1073/pnas.2218523120
Akses: Open Access ✓