Summary of Reducing Large Language Model Bias with Emphasis on ‘restricted Industries’: Automated Dataset Augmentation and Prejudice Quantification, by Devam Mondal et al.
Reducing Large Language Model Bias with Emphasis on ‘Restricted Industries’: Automated Dataset Augmentation and Prejudice Quantification
by Devam Mondal, Carlo Lipizzi
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 approach to debiasing large language models by introducing specified dataset augmentation techniques in the context of restricted industries with limited data. The authors aim to address concerns about biases developed by these models, which can have significant implications for various applications. The proposed mechanism uses bias producers as a lens to understand and mitigate biases. Additionally, two new metrics – the mb-index and db-index – are introduced to quantify bias, recognizing that it arises from both intrinsic model architecture and dataset factors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to fix a problem with big language models that can be biased. The authors want to make sure these models don’t learn bad habits from the data they’re trained on. They came up with a new way to make this happen by adding more specific types of data to the model’s training set. This could help in industries where there is limited data available, like healthcare or finance. The researchers also created two new tools to measure bias and understand where it comes from. |
