Summary of A New Pipeline For Generating Instruction Dataset Via Rag and Self Fine-tuning, by Chih-wei Song et al.
A New Pipeline For Generating Instruction Dataset via RAG and Self Fine-Tuning
by Chih-Wei Song, Yu-Kai Lee, Yin-Te Tsai
First submitted to arxiv on: 12 Aug 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 This research proposes a pipeline that leverages large language models (LLMs) and Retrieval-Augmented Generation to construct high-quality instruction datasets for fine-tuning on specific domains. The pipeline ingests domain-specific documents to generate relevant instructions, overcoming limitations of traditional dataset creation methods. This approach offers a dynamic solution that can adapt to updates in the document collection, eliminating the need for complete retraining. It also addresses data scarcity by generating instruction datasets from limited initial documents. As a case study, the approach is applied to psychiatry, a field requiring specialized knowledge and sensitive handling of patient information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research makes special language models that can help big companies and organizations with their unique needs. Instead of trying to be good at everything, these models focus on specific areas, like healthcare or finance. To make this happen, the researchers created a new way to gather instructions for training these models. They use large language models and special generation techniques to create high-quality instruction datasets that are perfect for fine-tuning on specific domains. This approach is better than other methods because it can adapt quickly to changes in the data and doesn’t need to start over from scratch. |
Keywords
» Artificial intelligence » Fine tuning » Retrieval augmented generation
