Summary of Exploring and Steering the Moral Compass Of Large Language Models, by Alejandro Tlaie
Exploring and steering the moral compass of Large Language Models
by Alejandro Tlaie
First submitted to arxiv on: 27 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 Medium Difficulty Summary: This study investigates the ethics of Large Language Models (LLMs) by comparing the moral profiles of several advanced models. The researchers subjected popular proprietary and open-source LLMs to a series of ethical dilemmas and found that proprietary models tend to prioritize utility, while open-source models align with values-based ethics. Notably, all models exhibited a strong liberal bias when tested using the Moral Foundations Questionnaire, except for Llama 2-7B. Furthermore, the study proposes a novel technique called similarity-specific activation steering, which allows for reliable manipulation of the model’s moral compass. The findings highlight the ethical implications of already deployed LLMs and emphasize the need to consider these aspects in their development and deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This research looks at how big language models think about right and wrong. It compares different types of models to see what they would do in tricky situations. The study found that some models are more focused on getting things done, while others care about being fair and kind. Most models also seem to have a strong sense of fairness and equality, but one model was different from the rest. The researchers also developed a new way to make these models behave in certain ways, which could be important for making decisions that affect people’s lives. |
Keywords
» Artificial intelligence » Llama
