Papers with end-to-end LLMs
A Cost-Efficient Modular Sieve for Extracting Product Information from Company Websites (2024.emnlp-industry)
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Anna Hätty, Dragan Milchevski, Kersten Döring, Marko Putnikovic, Mohsen Mesgar, Filip Novović, Maximilian Braun, Karina Borimann, Igor Stranjanac
| Challenge: | Existing methods for extracting product information are resource-intensive and computationally prohibitive due to website structure differences and numerous non-product pages. |
| Approach: | They propose a modular method that leverages low-cost classification models to filter out company web pages. |
| Outcome: | The proposed method improves on a new dataset of 7000 product and non-product web pages and reduces computational time and costs. |