Summary of Analyzing Large Language Models Chatbots: An Experimental Approach Using a Probability Test, by Melise Peruchini et al.
Analyzing Large language models chatbots: An experimental approach using a probability test
by Melise Peruchini, Julio Monteiro Teixeira
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 study explores the capabilities of two Large Language Models (LLMs) chatbots, ChatGPT and Gemini, in handling probability questions. The researchers designed prompts based on the “Linda Problem” from cognitive psychology and a new problem, the “Mary Problem”, to test the chatbots’ logical reasoning and textual understanding. By analyzing the dataset of chatbot interactions, the study reveals that while both models perform well on a familiar probabilistic problem, they struggle with new tests that require direct application of probabilistic logic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how two AI chatbots, ChatGPT and Gemini, answer probability questions. The scientists made special test prompts using famous cognitive psychology problems, like the “Linda Problem”. They want to see if these AI models use logical thinking or just memorize patterns from the test questions. The results show that both chatbots are good at answering familiar questions, but get stuck when trying new ones. |
Keywords
* Artificial intelligence * Gemini * Probability
