A Review of the Challenges with Massive Web-mined Corpora Used in Large Language Models - Pre-training - A Preprint

Sep 13, 2026

This article presents a comprehensive review of the challenges associated with using massive web-mined corpora for the pre-training of large language models (LLMs). The review highlights key issues including noise from irrelevant or misleading information, duplication, low-quality or incorrect data, biases, and the risk of sensitive or personal information. By examining existing methods for data cleaning and pre-processing, together with bias detection and mitigation, it identifies gaps in current approaches and outlines directions for future research. The discussion supports more accurate, reliable, and ethically responsible language model development.

LanguageEnglish

CategoryGeneral

FormatPDF

Pages8

Reading Time3 min

AccessFree to download

1 Introduction

2 The Nature of Web-mined Corpora

2.1 Defining Web-mined Corpora

2.2 Scale and Diversity

2.3 Widely used Web-mined Corpora

A REVIEW OF THE CHALLENGES WITH MASSIVE WEB-MINED CORPORA USED IN LARGE LANGUAGE MODELS PRE-TRAINING A PREPRINT arXiv :2407 .07630v 1 [ cs .CL] 10 Jul 2024 Michał Perełkiewicz National Information Processing Institute al. Niepodległoci 188B, Warsaw, Poland [mperelkiewicz@opi.org.pl](mperelkiewicz@opi.org.pl) Rafał Powiata National Information Processing Institute al. Niepodległoci 188B, Warsaw, Poland [rposwiata@opi.org.pl](rposwiata@opi.org.pl) July 11, 2024 ABSTRACT This article presents a comprehensive review of the challenges associated with using massive webmined corpora for the pre-training of large language models (LLMs) . This review identifies key challenges in this domain, including challenges such as noise (irrelevant or misleading information), duplication of content, the presence of low-quality or incorrect information, biases, and the inclusion of sensitive or personal information in web-mined corpora. Addressing these issues is crucial for the development of accurate, reliable, and ethically responsible language models. Through an examination of current methodologies for data cleaning, pre-processing, bias detection and mitigation, we highlight the gaps in existing approaches and suggest directions for future research. Our discussion aims to catalyze advancements in developing more sophisticated and ethically responsible LLMs. Keywords Natural Language Processing · Large Language Models Training · Web-mined Corpora 1 Introduction The advent of large language models (LLMs) has heralded a new era in natural language processing (NLP), offering capabilities that range from sophisticated text generation to nuanced language understanding. These advancements have been propelled by significant improvements in model architectures, algorithms, and, crucially, the availability of extensive datasets for training. Given the data-intensive nature of these models, the quest for high-quality, diverse, and substantial datasets has become paramount. In this context, massive web-mined corpora have emerged as a vital resource, offering an abundance of textual data that mirrors the vastness and variety of human language and interaction [22, 35, 37, 42] . The internet, with its exponential growth and dynamic content, presents a near-infinite source of text data, spanning every conceivable topic, language, and style. This richness makes web-mined data an attractive foundation for training LLMs, aiming to equip them with a broad understanding of language and its applications. However, the use of such data is not without its challenges. The process of web mining—extracting data from websites—entails navigating a complex landscape of technical, legal, ethical, and quality-related issues [12, 13, 15, 43, 46] . By critically examining the use of web-mined corpora in the pre-training of LLMs, this article contributes to a nuanced understanding of the current landscape and future directions in large-scale language model development. 2 The Nature of Web-mined Corpora The internet serves as an abundant source of data with great potential for use in training advanced artificial intelligence models. Data collections such as Common Crawl 1 , consisting of textual data scraped from the web, are a treasure trove of linguistic diversity and richness. This section briefly describes the characteristics of such corpora. 2.1 Defining Web-mined Corpora At its core, a web-mined corpus is a collection of textual data that has been extracted from the internet. This includes a wide range of content such as websites, blogs, forums, social media posts, and other digital texts. The process of web mining involves the automated scraping of this content, followed by stages of cleaning and pre-processing to prepare the data for use in machine learning applications. The primary allure of web-mined corpora lies in their ability to reflect the multifaceted nature of human language and communication as manifested online. 2.2 Sc

FAQs

What challenges arise when using massive web-mined corpora for LLM pre-training?

The article reviews major challenges such as noise, duplicate content, low-quality or incorrect information, biases, and the presence of sensitive or personal data. These issues affect accuracy, reliability, and ethics in training.

How do current methodologies address data cleaning and pre-processing for web-mined corpora?

The paper discusses approaches for cleaning and pre-processing, including filtering out irrelevant or low-quality text and removing boilerplate. It also reviews work on bias detection and mitigation.

What examples of web-mined corpora are commonly used in training LLMs?

The paper highlights Common Crawl and C4, and also mentions their multilingual variants such as mC4. It describes their coverage and how they are sourced and cleaned.

Sep 13, 2026

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