The application of large-scale language models is becoming increasingly widespread, and the screening process of pre-training data behind them has also attracted much attention. This article presents a study on the impact of data filtering on text. Using a new dataset and framework called AboutMe, the researchers analyzed text data from the "About Me" section of web pages to understand how data filtering affects the extraction and interpretation of information such as a website's author's interests, social roles, and geographic location. This study highlights the complexity of pre-training data screening and its potential social impact, providing important directions for future research.
With the advancement of natural language processing and natural language generation, large language models have been widely used in practical applications. Using a new dataset and framework called AboutMe, the researchers documented the impact of data filtering on text. By analyzing the "About Me" section of a web page, the research team measured information such as the interests, social role and geographic location of the website author. They highlight the complexity of the pretraining data sifting process and call for further research into its social implications.
This research is critical to understanding the training process of large language models and their potential biases and social impacts. Future research should further explore how to improve the data screening process to reduce bias and improve model fairness and reliability, ultimately promoting the healthy development and wider application of large language models.