Summary of Revealing Fine-grained Values and Opinions in Large Language Models, by Dustin Wright et al.
Revealing Fine-Grained Values and Opinions in Large Language Models
by Dustin Wright, Arnav Arora, Nadav Borenstein, Srishti Yadav, Serge Belongie, Isabelle Augenstein
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 explores ways to identify biases in large language models (LLMs) by analyzing their responses to survey questions. The authors propose a novel approach, prompting LLMs with the 62 propositions of the Political Compass Test (PCT), and analyze the generated stances using coarse-grained and fine-grained methods. Fine-grained analysis involves identifying tropes in plain text justifications, revealing patterns in LLM responses. The study finds that demographic features added to prompts significantly affect outcomes on the PCT, reflecting bias and disparities between closed-form and open-domain responses. Additionally, patterns in rationales via tropes show repeated justifications across models and prompts with disparate stances. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to find biases in language models that can be harmful. The authors use a special test called the Political Compass Test (PCT) to see how language models respond to different questions. They analyze these responses using two main methods: one looks at big patterns, and the other digs deeper into why the models give certain answers. They found that adding demographic information to the prompts makes a big difference in how the models answer the PCT questions. This shows that there are biases in the language models and that they can be influenced by what kind of information is given to them. |
Keywords
» Artificial intelligence » Prompting
