A Review of the Challenges with Massive Web-mined Corpora Used in Large Language Models Pre-Training
This is an incremental review that addresses data quality and ethical problems for researchers and developers working on large language models.
The paper reviews challenges in using massive web-mined corpora for pre-training large language models, identifying issues like noise, duplication, biases, and sensitive information, and suggests future research directions to improve model accuracy and ethics.
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.