Summary of Mapping and Influencing the Political Ideology Of Large Language Models Using Synthetic Personas, by Pietro Bernardelle et al.
Mapping and Influencing the Political Ideology of Large Language Models using Synthetic Personas
by Pietro Bernardelle, Leon Fröhling, Stefano Civelli, Riccardo Lunardi, Kevin Roitero, Gianluca Demartini
First submitted to arxiv on: 19 Dec 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 paper explores the impact of persona-based prompting on large language models’ political orientation. It uses a collection of synthetic personas and the Political Compass Test to analyze how LLMs respond to explicit ideological prompts towards diametrically opposed political orientations. The results show that most personas cluster in the left-libertarian quadrant, with models demonstrating varying degrees of responsiveness when prompted with ideological descriptors. While all models shift towards right-authoritarian positions, they exhibit more limited shifts towards left-libertarian positions, suggesting an asymmetric response to manipulation that may reflect inherent biases in model training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how big language models are influenced by our thoughts and feelings about politics. It uses special sets of descriptions called personas and a test called the Political Compass Test to see what happens when these models are given ideas that are very different from their usual way of thinking. The results show that most of these models tend to think in one way (left-libertarian) more than another way (right-authoritarian), even when they’re encouraged to think differently. This might mean that the models have biases built into them because of how they were trained. |
Keywords
» Artificial intelligence » Prompting
