# Enhancing Supply Chain Transparency in Emerging Economies Using Online Contents and LLMs

arXiv: https://arxiv.org/abs/2412.16922 · HTML: https://mlsanity.com/paper/2412.16922/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 22 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction](https://mlsanity.com/paper/2304.08085/) (2023)
- [Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!](https://mlsanity.com/paper/2303.08559/) (2023)
- [Enhancing Supply Chain Visibility with Knowledge Graphs and Large Language Models](https://mlsanity.com/paper/2408.07705/) (2024)
- [A Knowledge Graph Perspective on Supply Chain Resilience](https://mlsanity.com/paper/2305.08506/) (2023)

## Built on by (most cited first, 1 of 1)

- [InterCorpRel-LLM: Enhancing Financial Relational Understanding with Graph-Language Models](https://mlsanity.com/paper/2510.09735/) (2025)

## Abstract (arXiv)

In the current global economy, supply chain transparency plays a pivotal role in ensuring this security by enabling companies to monitor supplier performance and fostering accountability and responsibility. Despite the advancements in supply chain relationship datasets like Bloomberg and FactSet, supply chain transparency remains a significant challenge in emerging economies due to issues such as information asymmetry and institutional gaps in regulation. This study proposes a novel approach to enhance supply chain transparency in emerging economies by leveraging online content and large language models (LLMs). We develop a Supply Chain Knowledge Graph Mining System that integrates advanced LLMs with web crawler technology to automatically collect and analyze supply chain information. The system's effectiveness is validated through a case study focusing on the semiconductor supply chain, a domain that has recently gained significant attention due to supply chain risks. Our results demonstrate that the proposed system provides greater applicability for emerging economies, such as mainland China, complementing the data gaps in existing datasets. However, challenges including the accurate estimation of monetary and material flows, the handling of time series data, synonyms disambiguation, and mitigating biases from online contents still remains. Future research should focus on addressing these issues to further enhance the system's capabilities and broaden its application to other emerging economies and industries.