Summary of Autopuredata: Automated Filtering Of Undesirable Web Data to Update Llm Knowledge, by Praneeth Vadlapati
AutoPureData: Automated Filtering of Undesirable Web Data to Update LLM Knowledge
by Praneeth Vadlapati
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AutoPureData system aims to maintain the accuracy and relevance of up-to-date language models by automatically collecting and purifying web data. The system utilizes existing trusted AI models to eliminate unsafe and undesirable text with high accuracy, ensuring that only meaningful and safe text is used to update large language model (LLM) knowledge. This approach enables LLMs to fetch new data from a vector database and perform cross-lingual retrieval, enhancing their utility across various industries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The system collects web data, loads it into the trusted AI models, and successfully eliminates unsafe text with 97% accuracy and undesirable text with 86% accuracy. The purified text is then optimized and stored in a vector database for future querying. This approach ensures that LLMs can maintain their reliability by using only pure data to update their knowledge. |
Keywords
» Artificial intelligence » Large language model
