Summary of Llm-assisted Content Conditional Debiasing For Fair Text Embedding, by Wenlong Deng et al.
LLM-Assisted Content Conditional Debiasing for Fair Text Embedding
by Wenlong Deng, Blair Chen, Beidi Zhao, Chiyu Zhang, Xiaoxiao Li, Christos Thrampoulidis
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 paper proposes a novel method for learning fair text embeddings in Natural Language Processing (NLP) to mitigate biases in machine learning models. The authors introduce a content-conditional equal distance (CCED) fairness criterion, ensuring that sensitive attributes and text embeddings are independent. They also develop a content-conditional debiasing (CCD) loss to maintain the same distance between embeddings of texts with different sensitive attributes but identical content. To address insufficient training data, they use Large Language Models (LLMs) to fairly augment texts into different sensitive groups. The authors evaluate their approach extensively and demonstrate its effectiveness in enhancing fairness while maintaining utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure machine learning models are fair and don’t show biases towards certain people or groups. In language processing, it’s crucial to have fair text embeddings that can be used for real-world applications like search engines. The authors came up with a new way to learn these fair text embeddings by defining a special fairness standard called content-conditional equal distance (CCED). They also created a debiasing loss function and used large language models to create more training data that’s fair and balanced. The results show that their approach works well in making the text embeddings fair while still being useful. |
Keywords
* Artificial intelligence * Loss function * Machine learning * Natural language processing * Nlp
