Summary of A Comparison Of Large Language Model and Human Performance on Random Number Generation Tasks, by Rachel M. Harrison
A Comparison of Large Language Model and Human Performance on Random Number Generation Tasks
by Rachel M. Harrison
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Neurons and Cognition (q-bio.NC)
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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 paper explores whether a large language model (LLM) like ChatGPT-3.5 exhibits human-like cognitive biases when generating random number sequences. The authors adapted an existing human RNGT for the LLM-compatible environment, aiming to test its ability to avoid predictable patterns. Initial findings show that ChatGPT-3.5 outperforms humans in terms of repeat frequencies and adjacent number frequencies, demonstrating its potential to mimic human random generation behaviors. Further research is needed to understand how different models, parameters, and prompting methodologies can improve the LLM’s capabilities, potentially expanding its applications in cognitive and behavioral science research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at whether a special computer program called ChatGPT-3.5 can think like humans when generating random numbers. The researchers used an old way of testing human thinking for this computer program to see if it can do better than people. So far, the results show that the computer program is really good at not repeating itself and keeping the numbers in order. This means it might be useful for studying how people think and behave. |
Keywords
» Artificial intelligence » Large language model » Prompting
