Summary of Leveraging Large Language Models For Web Scraping, by Aman Ahluwalia et al.
Leveraging Large Language Models for Web Scraping
by Aman Ahluwalia, Suhrud Wani
First submitted to arxiv on: 12 Jun 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 The research investigates a general-purpose accurate data scraping recipe for RAG models designed for language generation, leveraging pre-trained Large Language Models (LLMs) and targeted information access enabled by Retrieval-Augmented Generation (RAG) models. The goal is to overcome limitations in direct application of LLMs for data extraction, prioritizing factual accuracy over fluency. The study uses a latent knowledge retriever, allowing the model to retrieve and attend to documents from a large corpus, and analyzes RAG model architecture under three tasks: semantic classification, chunking, and ranking algorithms. The results show that pre-trained LLMs with effective chunking, searching, and ranking algorithms can be efficient data scraping tools for extracting complex data from unstructured text. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers understand and extract information from big texts better. It uses special computer models to teach other models how to find specific things in a text quickly and accurately. The researchers tested these models on three tasks: identifying important parts of a text, breaking down a text into smaller pieces, and ranking the importance of different parts of a text. They found that by using existing language models and adding some extra steps, they can create a powerful tool for extracting information from big texts. |
Keywords
» Artificial intelligence » Classification » Rag » Retrieval augmented generation
