Advancements in Large Language Models (LLMs) enabled by Natural Language Processing and Generation have broad applications. However, their biased representations of human viewpoints stemming from pretraining data composition have prompted researchers to focus on data curation. A recent study introduces the AboutMe dataset to address these biases and the need for sociolinguistic analysis in NLP.
Advancements in Natural Language Processing and Generation
Large Language Models (LLMs) are increasingly used in various fields due to their ability to mimic human behavior. However, these models are influenced by biases in the pretraining data, impacting their behavior and viewpoints.
Addressing Bias in Language Models
Researchers are focusing on understanding and documenting the transformations made to the data before pretraining to mitigate bias. A recent study introduced a new dataset and framework called AboutMe, aiming to highlight and address the assumptions in data curation workflows.
Sociolinguistic Analysis and Data Filtering
The study utilized sociolinguistic analyses to understand the social and geographic contexts of web-scraped text, particularly from ‘about me’ pages. It also examined the effects of filtering on the kept or deleted pages, revealing implicit preferences and unintentional eliminations.
Implications for Language Model Development
The research emphasizes the need for more awareness and research on pretraining data curation procedures, especially regarding social factors. The team stresses the importance of understanding the consequences of data filtering on the portrayal of diverse viewpoints in language models.
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List of Useful Links:
- AI Lab in Telegram @aiscrumbot – free consultation
- Meet ‘AboutMe’: A New Dataset And AI Framework that Uses Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters
- MarkTechPost
- Twitter – @itinaicom
