  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). 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.
