Summary of How Random Is Random? Evaluating the Randomness and Humaness Of Llms’ Coin Flips, by Katherine Van Koevering et al.
How Random is Random? Evaluating the Randomness and Humaness of LLMs’ Coin Flips
by Katherine Van Koevering, Jon Kleinberg
First submitted to arxiv on: 31 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This research investigates the tendency of Large Language Models (LLMs) to generate patterns, even when there should be no pattern, due to their reliance on human data and biases. Specifically, the study examines how LLMs approach randomness by generating binary random sequences, a well-studied phenomenon. The findings reveal that GPT-4 and LLaMA-3 tend to exhibit and exacerbate human biases in this context, whereas GPT-3.5 displays more random behavior. This dichotomy between randomness and humanity is proposed as a fundamental question of LLMs, suggesting that either behavior may be useful in different situations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are special because they can’t be random like humans are. We see patterns where there aren’t any and we do it predictably. Researchers tested how well these language models create random sequences by using 0s and 1s. They found that some models, like GPT-4 and LLaMA-3, copy human biases and make mistakes, while another model, GPT-3.5, is more random. This difference between being random or acting like humans might be useful in different situations. |
Keywords
» Artificial intelligence » Gpt » Llama
