Summary of Duplicate Detection with Genai, by Ian Ormesher
Duplicate Detection with GenAI
by Ian Ormesher
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 explores ways to improve the process of detecting and removing duplicate customer records in Customer Relations Management systems. Current methods rely on traditional Natural Language Processing (NLP) techniques, but these have limitations, resulting in only a 30% accuracy rate for data de-duplication. The proposed approach utilizes Large Language Models and Generative AI to significantly boost this rate to almost 60%, as demonstrated on common benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses advanced computer models to fix duplicate customer records on computers. Right now, people manually enter information about customers into these systems, which can cause problems like duplicates. This makes it hard for businesses to keep track of their customers. The current way to find and remove duplicates is by using special language processing techniques. But even with these methods, the accuracy rate is only 30%. This paper shows how to use new kinds of AI models to improve this process, getting the accuracy up to almost 60% on standard tests. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
