Large Language Models (LLMs) demonstrate remarkable capabilities in
replicating human tasks and boosting productivity. However, their direct
application for data extraction presents limitations due to a prioritisation of
fluency over factual accuracy and a restricted ability to manipulate specific
information. Therefore to overcome these limitations, this research leverages
the knowledge representation power of pre-trained LLMs and the targeted
information access enabled by RAG models, this research investigates a
general-purpose accurate data scraping recipe for RAG models designed for
language generation. To capture knowledge in a more modular and interpretable
way, we use pre trained language models with a latent knowledge retriever,
which allows the model to retrieve and attend over documents from a large
corpus. We utilised RAG model architecture and did an in-depth analysis of
their capabilities under three tasks: (i) Semantic Classification of HTML
elements, (ii) Chunking HTML text for effective understanding, and (iii)
comparing results from different LLMs and ranking algorithms. While previous
work has developed dedicated architectures and training procedures for HTML
understanding and extraction, we show that LLMs pre-trained on standard natural
language with an addition of effective chunking, searching and ranking
algorithms, can prove to be efficient data scraping tool to extract complex
data from unstructured text. Future research directions include addressing the
challenges of provenance tracking and dynamic knowledge updates within the
proposed RAG-based data extraction framework. By overcoming these limitations,
this approach holds the potential to revolutionise data extraction from vast
repositories of textual information.

使用已经训练好的大型语言模型与 RAG 模型结合，研究了一种通用准确的数据抓取方法，该方法能够从非结构化文本中提取复杂数据。未来的研究方向包括解决溯源追踪和动态知识更新等挑战，在提取数据的方法和框架方面潜力巨大。

利用大型语言模型进行网络抓取

Leveraging Large Language Models for Web Scraping

The prevalence of data scraping from social media as a means to obtain
datasets has led to growing concerns regarding unauthorized use of data. Data
poisoning attacks have been proposed as a bulwark against scraping, as they
make data "unlearnable" by adding small, imperceptible perturbations.
Unfortunately, existing methods require knowledge of both the target
architecture and the complete dataset so that a surrogate network can be
trained, the parameters of which are used to generate the attack. In this work,
we introduce autoregressive (AR) poisoning, a method that can generate poisoned
data without access to the broader dataset. The proposed AR perturbations are
generic, can be applied across different datasets, and can poison different
architectures. Compared to existing unlearnable methods, our AR poisons are
more resistant against common defenses such as adversarial training and strong
data augmentations. Our analysis further provides insight into what makes an
effective data poison.

该研究介绍了自回归（AR）中毒的方法，可以生成具有毒性的数据，而不需要访问更广泛的数据集，比起现有的不可学习方法，我们的 AR 毒药更加抵抗对抗训练以及强数据扩充等常见的防御。