Summary of Gpt-ology, Computational Models, Silicon Sampling: How Should We Think About Llms in Cognitive Science?, by Desmond C. Ong
GPT-ology, Computational Models, Silicon Sampling: How should we think about LLMs in Cognitive Science?
by Desmond C. Ong
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the state of Large Language Models (LLMs) in cognitive science. It reviews three emerging research paradigms: GPT-ology, LLMs-as-computational-models, and “silicon sampling.” The authors analyze recent papers that utilize LLMs under these paradigms, highlighting their claims and challenges to scientific inference. They identify outstanding issues, such as closed-source vs open-sourced models, training data transparency, and reproducibility in LLM research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores the use of Large Language Models (LLMs) in cognitive science. It looks at three new ways scientists are studying LLMs: GPT-ology, treating LLMs as computational models, and “silicon sampling.” The authors examine recent studies that used these approaches and discuss their findings. They also talk about some big problems that need to be solved, like keeping the data and code secret or open, making sure research is repeatable, and figuring out how to use LLMs for new tasks. |
Keywords
» Artificial intelligence » Gpt » Inference
