Summary of Supply Chain Network Extraction and Entity Classification Leveraging Large Language Models, by Tong Liu et al.
Supply Chain Network Extraction and Entity Classification Leveraging Large Language Models
by Tong Liu, Hadi Meidani
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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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 approach leverages large language models (LLMs) to extract and process raw textual information from publicly available sources, constructing a comprehensive supply chain graph for the civil engineering sector. The paper focuses on mapping relationships and identifying entities’ roles within this network. By fine-tuning an LLM model, the authors achieve improved entity classification accuracy, providing insights into hidden relationships among companies, projects, and other entities. This novel approach has the potential to revolutionize industry-specific supply chain analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Supply chain networks are important for industries, but they can be complicated. Right now, we don’t have good ways to map these networks and understand who’s working together. Traditional methods rely on structured data and human effort, which limits their scope. New technologies in language processing (NLP) offer a way to analyze supply chains using unstructured text data. This paper uses large language models to extract information from publicly available sources and create a big picture of the civil engineering sector’s supply chain. The authors also fine-tune this model to better understand who’s working together and their roles within the network. |
Keywords
» Artificial intelligence » Classification » Fine tuning » Nlp
