تقرير
Teaching Specific Scientific Knowledge into Large Language Models through Additional Training
| العنوان: | Teaching Specific Scientific Knowledge into Large Language Models through Additional Training |
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| المؤلفون: | Hatakeyama-Sato, Kan, Igarashi, Yasuhiko, Katakami, Shun, Nabae, Yuta, Hayakawa, Teruaki |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | Through additional training, we explore embedding specialized scientific knowledge into the Llama 2 Large Language Model (LLM). Key findings reveal that effective knowledge integration requires reading texts from multiple perspectives, especially in instructional formats. We utilize text augmentation to tackle the scarcity of specialized texts, including style conversions and translations. Hyperparameter optimization proves crucial, with different size models (7b, 13b, and 70b) reasonably undergoing additional training. Validating our methods, we construct a dataset of 65,000 scientific papers. Although we have succeeded in partially embedding knowledge, the study highlights the complexities and limitations of incorporating specialized information into LLMs, suggesting areas for further improvement. Comment: added token information for some texts, and fixed typo |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2312.03360 |
| رقم الأكسشن: | edsarx.2312.03360 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |