Summary of Llm-based Robust Product Classification in Commerce and Compliance, by Sina Gholamian et al.
LLM-Based Robust Product Classification in Commerce and Compliance
by Sina Gholamian, Gianfranco Romani, Bartosz Rudnikowicz, Stavroula Skylaki
First submitted to arxiv on: 11 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This machine learning paper proposes a new approach to automatic product classification for international trade, tackling challenges like incomplete and abbreviated product descriptions. The authors explore the limitations of current methods and propose innovative techniques using Large Language Models (LLMs) to improve robustness in the presence of incomplete data. They demonstrate that LLMs with in-context learning outperform supervised approaches in clean-data scenarios and are more resilient to data attacks. The paper contributes to the development of e-commerce platforms and enterprises involved in international trade, enhancing product classification accuracy and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve automatic product classification for international trade by using machine learning. Right now, people classify products manually, which is slow and prone to errors. This makes it hard to handle all the products being imported and exported. The authors are trying to find a better way to do this using special language models that can learn from incomplete data. They want to see if these models can work well even when product descriptions are missing or unclear. Their results show that these models can be very good at classifying products, even with incomplete information. This could make international trade more efficient and accurate. |
Keywords
» Artificial intelligence » Classification » Machine learning » Supervised
