Summary of Prism: a Methodology For Auditing Biases in Large Language Models, by Leif Azzopardi and Yashar Moshfeghi
PRISM: A Methodology for Auditing Biases in Large Language Models
by Leif Azzopardi, Yashar Moshfeghi
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 This paper presents a novel approach called PRISM for auditing large language models (LLMs) to uncover their biases and preferences. Unlike existing methods that directly ask LLMs about their opinions, PRISM uses task-based inquiry prompting to indirectly elicit their positions on various topics. The authors demonstrate the effectiveness of PRISM by applying it to the Political Compass Test, which assesses the political leanings of 21 LLMs from seven providers. The results show that most LLMs tend to be economically left-leaning and socially liberal, with some models being more constrained in their responses than others. This study highlights the importance of auditing LLMs to understand their biases and limitations, which is crucial for developing responsible artificial intelligence (AI). Keywords: large language models, bias detection, task-based inquiry, PRISM. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to figure out what a super smart computer program thinks about politics. That’s kind of what this paper is about. The authors created a new way called PRISM to find out what these programs think without directly asking them. They tested it on 21 language models from different companies and found that most of them tend to be more liberal and left-leaning when it comes to economics and social issues. Some programs are more willing to share their opinions than others, but overall, this study shows how important it is to understand what these powerful machines think before we can use them responsibly. |
Keywords
» Artificial intelligence » Prompting
