Summary of Cognitive Phantoms in Llms Through the Lens Of Latent Variables, by Sanne Peereboom et al.
Cognitive phantoms in LLMs through the lens of latent variables
by Sanne Peereboom, Inga Schwabe, Bennett Kleinberg
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 study investigates the validity of administering personality questionnaires to large language models (LLMs) and explores their latent structures. Recent studies have reported human-like traits in LLMs, but this approach suffers from a validity problem due to its assumption that these traits exist and can be measured with tools designed for humans. To address this issue, the study compares the latent structures of personality between humans and three LLMs using two validated questionnaires. The findings suggest that questionnaires designed for humans do not validly measure similar constructs in LLMs, and that these constructs may not exist in LLMs at all. This highlights the need for psychometric analyses of LLM responses to avoid chasing cognitive phantoms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being used more and more in real-life applications. To understand how they work, researchers are trying to figure out what makes them tick. One way they do this is by giving the models personality tests that were originally designed for humans. But there’s a problem with this approach: it assumes that these models have personalities like humans do. And even if they did, would the same questions and answers make sense for them? This study looks at how well these personality tests work on large language models. The results show that these tests don’t actually measure the same things in computers as they do in people. This means we need to come up with new ways of understanding what’s going on inside those computer brains. |
