Summary of Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection For Enhancing Large Language Models, by Jiaxin Zhang et al.
Synthetic Knowledge Ingestion: Towards Knowledge Refinement and Injection for Enhancing Large Language Models
by Jiaxin Zhang, Wendi Cui, Yiran Huang, Kamalika Das, Sricharan Kumar
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); 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 novel synthetic knowledge ingestion method, Ski, leverages fine-grained synthesis, interleaved generation, and assemble augmentation strategies to construct high-quality data representations from raw knowledge sources. Ski is integrated with three knowledge injection techniques: Retrieval Augmented Generation (RAG), Supervised Fine-tuning (SFT), and Continual Pre-training (CPT) to inject and refine knowledge in language models. The method is tested on various question-answering tasks spanning finance, biomedicine, and open-generation domains, demonstrating significant performance improvements over baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers learn more accurate information from the internet! Right now, these large language models are really good at remembering facts they’ve seen before. But it’s hard for them to learn new things or combine old knowledge with new information. The Ski method helps fix this problem by creating high-quality data representations from scratch. This means that when we want computers to learn something new, they can use the Ski method to build a strong foundation of knowledge. In experiments, using Ski helped language models answer questions more accurately on topics like finance and medicine. |
Keywords
» Artificial intelligence » Fine tuning » Question answering » Rag » Retrieval augmented generation » Supervised
