Summary of Evaluating Moral Beliefs Across Llms Through a Pluralistic Framework, by Xuelin Liu et al.
Evaluating Moral Beliefs across LLMs through a Pluralistic Framework
by Xuelin Liu, Yanfei Zhu, Shucheng Zhu, Pengyuan Liu, Ying Liu, Dong Yu
First submitted to arxiv on: 6 Nov 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 introduces a novel framework for evaluating the moral beliefs of large language models. A three-module approach is used to analyze four prominent models: ChatGPT, Gemini, Ernie, and ChatGLM. The framework involves constructing a dataset of 472 moral choice scenarios in Chinese, which are then used to assess the decision-making processes of the models. The results show that English models tend to mirror individualistic moral beliefs, while Chinese models exhibit collectivist tendencies and ambiguity in their moral choices. The study also uncovers gender bias within the moral beliefs of all examined language models. This methodology offers an innovative means for comparing moral values across different cultures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how big language models make decisions based on what’s right or wrong. They created a special dataset with scenarios that test these models’ moral choices. The results show that some models, like those made in English, tend to make individualistic choices, while others, made in Chinese, are more collectivist. The study also found that all the models had gender bias in their moral beliefs. This new way of looking at how language models think can help us understand moral values across different cultures. |
Keywords
» Artificial intelligence » Gemini
