Summary of Extended Japanese Commonsense Morality Dataset with Masked Token and Label Enhancement, by Takumi Ohashi et al.
Extended Japanese Commonsense Morality Dataset with Masked Token and Label Enhancement
by Takumi Ohashi, Tsubasa Nakagawa, Hitoshi Iyatomi
First submitted to arxiv on: 12 Oct 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 presents a crucial step towards integrating moral reasoning into AI systems, which is essential in today’s rapidly advancing artificial intelligence landscape. The authors expand the existing JCommonsenseMorality (JCM) dataset by proposing a novel sentence expansion method called Masked Token and Label Enhancement (MTLE). This approach selectively masks important parts of sentences related to moral judgment and replaces them with alternative expressions generated by a large language model, while re-assigning appropriate labels. The extended JCM (eJCM) dataset contains 31,184 sentences, an increase from the original 13,975 sentences. The authors train a model using eJCM and compare its performance to other models trained on different datasets, including ChatGPT one-shot classification, AugGPT, and GPT-4 Turbo. The results demonstrate the effectiveness of the eJCM dataset in improving AI systems’ ability to perform complex moral reasoning tasks unique to Japanese culture. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers have created a new way to teach artificial intelligence about right and wrong by expanding a special database called JCommonsenseMorality (JCM). They added more sentences to make it bigger and better. The new database is called eJCM. It has over 31,000 sentences, which is much bigger than the original 14,000 sentences. The researchers used this new database to train an AI model and tested it against other models. Their results showed that their model was really good at making moral decisions, especially when it came to Japanese culture. |
Keywords
» Artificial intelligence » Classification » Gpt » Large language model » One shot » Token
