Summary of Dopamine: Domain-specific Pre-training Adaptation From Seed-guided Data Mining, by Vinayak Arannil et al.
DoPAMine: Domain-specific Pre-training Adaptation from seed-guided data Mining
by Vinayak Arannil, Neha Narwal, Sourav Sanjukta Bhabesh, Sai Nikhil Thirandas, Darren Yow-Bang Wang, Graham Horwood, Alex Anto Chirayath, Gouri Pandeshwar
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed framework, DoPAMine:Domain-specific Pre-training Adaptation from seed-guided data Mining, leverages a Large Language Model’s parametric knowledge to generate diverse and representative seed data tailored to a specific domain. This is then used to mine real-world data from a large corpus like Common Crawl for domain adaptation of the LM. The framework shows improved performance in continual pre-training settings, boosting the average performance by 4.9% and 5.1% on healthcare tasks and 2.9% and 6.7% on finance tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research proposes a new way to help Large Language Models learn about specific areas like healthcare or finance. The model uses seed data from these domains to mine more information from the internet, making it better at understanding and generating text related to these topics. This could be very useful for applications like customer service chatbots or medical report analysis. |
Keywords
» Artificial intelligence » Boosting » Domain adaptation » Large language model
