Summary of Kcmf: a Knowledge-compliant Framework For Schema and Entity Matching with Fine-tuning-free Llms, by Yongqin Xu et al.
KcMF: A Knowledge-compliant Framework for Schema and Entity Matching with Fine-tuning-free LLMs
by Yongqin Xu, Huan Li, Ke Chen, Lidan Shou
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); 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 paper presents a novel approach called Knowledge-Compliant Matching Framework (KcMF) for schema matching (SM) and entity matching (EM) tasks, which are essential for data integration. KcMF uses large language models (LLMs) to address hallucinations and confusion about task instructions without requiring domain-specific fine-tuning. The framework employs a pseudo-code-based strategy to decompose tasks into natural language statements that guide LLM reasoning. Additionally, the paper proposes Dataset as Knowledge (DaK) and Example as Knowledge (EaK) mechanisms to build domain knowledge sets when unstructured domain knowledge is lacking. A result-ensemble strategy is also introduced to leverage multiple knowledge sources and suppress badly formatted outputs. The paper’s results demonstrate that KcMF enhances five LLM backbones in both SM and EM tasks, outperforming non-LLM competitors by an average F1-score of 17.93%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at matching data from different sources. This is important because it helps us combine information from different places into a single picture. The problem is that current computer models can get confused and make mistakes. The new approach, called KcMF, uses a special way of breaking down tasks into simple language that the computer can understand. It also finds ways to help the computer learn from examples and datasets when it doesn’t have enough information. The results show that this approach is better than others at matching data and makes fewer mistakes. |
Keywords
» Artificial intelligence » F1 score » Fine tuning
