Summary of Do Large Language Models Exhibit Cognitive Dissonance? Studying the Difference Between Revealed Beliefs and Stated Answers, by Manuel Mondal et al.
Do Large Language Models Exhibit Cognitive Dissonance? Studying the Difference Between Revealed Beliefs and Stated Answers
by Manuel Mondal, Ljiljana Dolamic, Gérôme Bovet, Philippe Cudré-Mauroux, Julien Audiffren
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 investigates whether Large Language Models (LLMs) can perform causal reasoning or grasp uncertainty beyond tailored prompting and multiple-choice questions. To achieve this, it reformulates these issues as direct text completion tasks and compares the LLM’s predicted answers with its computed probability distributions over possible outcomes. The findings suggest that the LLM’s revealed beliefs differ significantly from its stated answer, indicating biases and misrepresentations in many scenarios. This challenges common evaluation methods and highlights the need for further research to assess the extent and nature of LLM capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computer programs that can understand and generate human-like text. Some people test these models by asking them questions or giving them choices, like a multiple-choice test. But this paper asks: what if we don’t ask silly questions? What if we just give the model some text and ask it to finish it? The researchers found that when they did this, the model’s answers didn’t always match its own predictions about how likely each answer was. This means that these models might not be as good at understanding or making decisions as we thought. |
Keywords
» Artificial intelligence » Probability » Prompting
