# AutoPureData: Automated Filtering of Undesirable Web Data to Update LLM Knowledge

arXiv: https://arxiv.org/abs/2406.19271 · HTML: https://mlsanity.com/paper/2406.19271/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 27 Jun 2024.

## Builds on (most cited first, 13 of 13)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://mlsanity.com/paper/2406.17557/) (2024)
- [Textbooks Are All You Need](https://mlsanity.com/paper/2306.11644/) (2023)
- [Challenges and Applications of Large Language Models](https://mlsanity.com/paper/2307.10169/) (2023)
- [Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://mlsanity.com/paper/2312.05934/) (2023)
- [Large Language Models Meet NLP: A Survey](https://mlsanity.com/paper/2405.12819/) (2024)
- [What's in the Box? A Preliminary Analysis of Undesirable Content in the Common Crawl Corpus](https://mlsanity.com/paper/2105.02732/) (2021)
- [Data-Juicer: A One-Stop Data Processing System for Large Language Models](https://mlsanity.com/paper/2309.02033/) (2023)
- [Automated Data Curation for Robust Language Model Fine-Tuning](https://mlsanity.com/paper/2403.12776/) (2024)
- [LLMClean: Context-Aware Tabular Data Cleaning via LLM-Generated OFDs](https://mlsanity.com/paper/2404.18681/) (2024)
- [SHED: Shapley-Based Automated Dataset Refinement for Instruction Fine-Tuning](https://mlsanity.com/paper/2405.00705/) (2024)
- [Web Content Filtering through knowledge distillation of Large Language Models](https://mlsanity.com/paper/2305.05027/) (2023)
- [Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users](https://mlsanity.com/paper/2311.05903/) (2023)

## Built on by (most cited first, 1 of 1)

- [3D-MoRe: Unified Modal-Contextual Reasoning for Embodied Question Answering](https://mlsanity.com/paper/2507.12026/) (2025)

## Abstract (arXiv)

Up-to-date and reliable language models are consistently sought after and are essential in various applications. Typically, models are trained on a fixed dataset and then deployed globally. However, the knowledge of the models becomes outdated. Enabling automatic updation of AI knowledge using web data involves significant concerns regarding the model's safety and quality due to a threat from unsafe and undesirable text across the web. The purity of new data was essential for updating knowledge of language models to maintain their reliability. This paper proposes AutoPureData, a system that automatically collects and purifies web data. The system loaded a sample of web data. Utilizing existing trusted AI models, it successfully eliminated unsafe text with an accuracy of 97% and undesirable text with an accuracy of 86%, demonstrating the system's effectiveness in purifying the data. The system ensures that only meaningful and safe text can be used to update LLM knowledge. The pure text was then optimized and stored in a vector database for future querying. It was found that LLM can fetch new data from the vector DB. The LLM writes the RAG query in English, even if the user's query is in another language, proving that the system can perform cross-lingual retrieval. This paper proposes a method to maintain the accuracy and relevance of up-to-date language models by ensuring that only purified data was used to update LLM knowledge. This work contributes to updating knowledge of chatbots using meaningful and safe text, enhancing their utility across various industries, and potentially reducing the risks associated with outputs caused by unsafe or impure data. Code is available at github.com/Pro-GenAI/AutoPureData.