Summary of Pretraining and Updates Of Domain-specific Llm: a Case Study in the Japanese Business Domain, by Kosuke Takahashi et al.
Pretraining and Updates of Domain-Specific LLM: A Case Study in the Japanese Business Domain
by Kosuke Takahashi, Takahiro Omi, Kosuke Arima, Tatsuya Ishigaki
First submitted to arxiv on: 12 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 The paper presents research on developing Large Language Models (LLMs) in Japanese for understanding business-related documents, such as news articles, technical reports, and patents. The LLM is designed to improve question answering accuracy without losing general knowledge. To address the problem of regular updates incorporating recent knowledge, experiments were conducted using a benchmark dataset for question answering in the target domain. Results show that the pretrained model improves QA accuracy and that a mix of latest and older texts in training data is necessary. The study’s findings contribute to the development of LLMs for various languages and domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can learn to read and understand Japanese business news, reports, and patents. Researchers created a special kind of AI model that gets better at answering questions about business without forgetting general knowledge. They also tested whether this model could be updated with new information from recent articles, which is important for keeping the model accurate over time. The study’s results show that this model can improve question-answering accuracy and that it’s essential to use a mix of old and new information when updating the model. |
Keywords
» Artificial intelligence » Question answering
