Summary of Does Cross-cultural Alignment Change the Commonsense Morality Of Language Models?, by Yuu Jinnai
Does Cross-Cultural Alignment Change the Commonsense Morality of Language Models?
by Yuu Jinnai
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper investigates the effect of aligning Japanese language models with English resources on their ability to understand common sense moral principles. The authors evaluate the performance of fine-tuned models using two datasets, JCommonsenseMorality (JCM) and ETHICS, and find that while the fine-tuning process improves model performance, it does not necessarily lead to alignment with Japanese culture. Specifically, the results show that a model fine-tuned using the JCM dataset outperforms one fine-tuned using English resources, suggesting that some aspects of commonsense morality are more transferable than others. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we can make language models better understand what people find moral or not. Right now, most work on this is done in English, and it mostly reflects the opinions of English-speaking people. But what if we want to use these models for languages other than English? The authors explore whether using English resources to align a Japanese language model is fair and effective. They test different approaches and find that while fine-tuning helps, it’s not enough to make the model truly understand Japanese values. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Language model
