Summary of (ir)rationality and Cognitive Biases in Large Language Models, by Olivia Macmillan-scott and Mirco Musolesi
(Ir)rationality and Cognitive Biases in Large Language Models
by Olivia Macmillan-Scott, Mirco Musolesi
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 research investigates whether large language models (LLMs) demonstrate rational reasoning, considering their potential biases. The study evaluates seven LLMs using cognitive psychology tasks and finds that they exhibit irrationality, similar to humans. However, the incorrect answers provided by LLMs differ from human-like biases. Additionally, the models show a layer of inconsistency in their responses. This paper contributes methodologically by developing ways to assess and compare different LLM capabilities, such as rational reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are really smart computers that can understand and generate human-like text. Some people think they might be able to reason like humans too. But do they really? Scientists tested seven of these models using special tasks designed to see how well they can think. The results showed that the models aren’t very good at reasoning either – they make mistakes, just like humans! But the way they make those mistakes is different from what we might expect. This study helps us understand how we can measure and compare the abilities of these powerful machines. |
