Summary of Way to Specialist: Closing Loop Between Specialized Llm and Evolving Domain Knowledge Graph, by Yutong Zhang et al.
Way to Specialist: Closing Loop Between Specialized LLM and Evolving Domain Knowledge Graph
by Yutong Zhang, Lixing Chen, Shenghong Li, Nan Cao, Yang Shi, Jiaxin Ding, Zhe Qu, Pan Zhou, Yang Bai
First submitted to arxiv on: 28 Nov 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 Way-to-Specialist (WTS) framework synergizes retrieval-augmented generation with knowledge graphs to enhance the specialized capability of large language models (LLMs) without requiring domain-specific training. WTS combines two components: DKG-Augmented LLM and LLM-Assisted DKG Evolution. The former retrieves domain knowledge from a domain knowledge graph (DKG) and uses it to prompt the LLM, while the latter leverages the LLM to generate new domain knowledge from processed tasks and updates the DKG. This framework enables continuous improvement in domain specialization as it answers and learns from domain-specific questions. Experimental results on 6 datasets spanning 5 domains show that WTS surpasses previous state-of-the-art (SOTA) in 4 specialized domains, achieving a maximum performance improvement of 11.3%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Way-to-Specialist framework helps large language models be better at solving problems that need special knowledge. Right now, these models are good at many things, but not great at using their own knowledge to solve problems. This framework connects the model to a special kind of map called a domain knowledge graph, which has information about a specific area or topic. The model uses this information to get better at solving problems in that area. Then, it takes what it learns and updates the map, so it gets even better over time. The results show that this framework works well for many different areas of study. |
Keywords
» Artificial intelligence » Knowledge graph » Prompt » Retrieval augmented generation
