Summary of Eliciting Personality Traits in Large Language Models, by Airlie Hilliard et al.
Eliciting Personality Traits in Large Language Models
by Airlie Hilliard, Cristian Munoz, Zekun Wu, Adriano Soares Koshiyama
First submitted to arxiv on: 13 Feb 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 Large Language Models (LLMs) are increasingly used in recruitment contexts, raising ethical concerns about transparency. While previous studies provided LLMs with personality assessments to complete, this study examines output variations based on different input prompts to better understand these models. We used novel elicitation approaches and prompts derived from common interview questions or designed to elicit specific Big Five personality traits to measure the models’ personalities based on their outputs. Our results show that all LLMs generally demonstrate high openness and low extraversion, but newer models with more parameters exhibit a broader range of personality traits. We also found positive associations between parameter size and openness and conscientiousness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are being used in job searches, but it raises questions about how transparent they are. Some studies gave the models personality tests to do, but this study looks at how different prompts affect what they say. They used special techniques and interview-style questions or ones designed to bring out certain personality traits. The goal is to see if these language models act like humans when given certain prompts. The results show that most language models are open-minded but not very outgoing. Newer, more powerful models show a wider range of personalities. |
