Summary of Understanding Intrinsic Socioeconomic Biases in Large Language Models, by Mina Arzaghi et al.
Understanding Intrinsic Socioeconomic Biases in Large Language Models
by Mina Arzaghi, Florian Carichon, Golnoosh Farnadi
First submitted to arxiv on: 28 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) are being increasingly used in decision-making processes, such as loan approvals and visa applications, which can lead to biased outcomes. The paper examines the relationship between demographic attributes and socioeconomic biases in LLMs, a crucial yet understudied area of fairness in LLMs. It introduces a novel dataset to quantify socioeconomic biases across various demographic groups. Findings reveal pervasive socioeconomic biases in established models like GPT-2 and state-of-the-art models like Llama 2 and Falcon. The study demonstrates that these biases are amplified when considering intersectionality, highlighting the need for bias mitigation techniques to ensure fairness in real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are being used more and more to make big decisions. But sometimes they can be unfair because of biases. This paper looks at how LLMs treat people from different backgrounds and how this affects their judgments. It creates a new dataset to study these biases and finds that many models, including GPT-2 and Llama 2, have biases against certain groups. The research shows that when we combine different characteristics about someone (like their name), the biases can get even worse. This means we need to find ways to make sure our AI systems are fair and don’t discriminate against people. |
Keywords
» Artificial intelligence » Gpt » Llama
