Summary of Dsg-kd: Knowledge Distillation From Domain-specific to General Language Models, by Sangyeon Cho et al.
DSG-KD: Knowledge Distillation from Domain-Specific to General Language Models
by Sangyeon Cho, Jangyeong Jeon, Dongjoon Lee, Changhee Lee, Junyeong Kim
First submitted to arxiv on: 23 Sep 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 proposed methodology for domain knowledge transfer uses knowledge distillation to infuse general language models with domain-specific knowledge via fine-tuning. This approach outperforms baseline models on Korean pediatric emergency department electronic medical record data and has broader applicability in various professional and technical domains. The study investigates emergency/non-emergency classification tasks based on EMR data, revealing that existing domain-specific pre-trained language models underperform compared to general language models in handling free-text data characteristics of non-English-speaking regions. The proposed method enhances classification performance by leveraging knowledge distillation between a general language model (student) and a domain-specific pre-trained model (teacher). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how to transfer specialized knowledge between language models. It uses something called “knowledge distillation” to help general language models understand more about specific areas, like pediatric emergency departments in Korea. This can make it easier for machines to understand medical records and do tasks like classifying emergencies correctly. The researchers found that this approach works better than others when dealing with data from non-English-speaking regions. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Knowledge distillation » Language model
