Summary of Investigating Privacy Bias in Training Data Of Language Models, by Yan Shvartzshnaider and Vasisht Duddu
Investigating Privacy Bias in Training Data of Language Models
by Yan Shvartzshnaider, Vasisht Duddu
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Computers and Society (cs.CY)
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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 This research paper investigates the privacy biases exhibited by Large Language Models (LLMs) when integrated into sociotechnical systems. The authors examine how LLMs acquire information from large amounts of non-publicly available training data, which may lead to a skew in the appropriateness of information flows within a given context. This skew can either align with existing expectations or signal systemic issues reflected in the training datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how language models learn from secret data and how this affects what they share with others. The researchers want to know if these models are fair and unbiased, or if they pick up biases from the data they were trained on. They think that understanding these biases is important for using language models in real-life situations. |
