Summary of Ethical Reasoning and Moral Value Alignment Of Llms Depend on the Language We Prompt Them In, by Utkarsh Agarwal et al.
Ethical Reasoning and Moral Value Alignment of LLMs Depend on the Language we Prompt them in
by Utkarsh Agarwal, Kumar Tanmay, Aditi Khandelwal, Monojit Choudhury
First submitted to arxiv on: 29 Apr 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 This paper investigates how Large Language Models (LLMs) perform ethical reasoning in different languages and whether their moral judgments depend on the language they are prompted in. The study builds upon Rao et al.’s framework for probing LLMs with ethical dilemmas and policies from deontology, virtue, and consequentialism. Three prominent LLMs – GPT-4, ChatGPT, and Llama2-70B-Chat – are evaluated across six languages: English, Spanish, Russian, Chinese, Hindi, and Swahili. The results show that GPT-4 is the most consistent and unbiased ethical reasoner across languages, while ChatGPT and Llama2-70B-Chat demonstrate significant moral value bias when prompted in languages other than English. Interestingly, this bias varies significantly across languages for all LLMs, including GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how computers can make good decisions based on rules. These computers are called Large Language Models (LLMs). The study wants to know if these computers think differently depending on the language they’re asked in. They test three of these computer models – GPT-4, ChatGPT, and Llama2-70B-Chat – in six different languages: English, Spanish, Russian, Chinese, Hindi, and Swahili. The results show that one model, GPT-4, makes good decisions no matter what language it’s asked in. The other two models make biased choices when asked in languages they’re not familiar with. |
Keywords
» Artificial intelligence » Gpt
