Summary of Exploring Accuracy-fairness Trade-off in Large Language Models, by Qingquan Zhang et al.
Exploring Accuracy-Fairness Trade-off in Large Language Models
by Qingquan Zhang, Qiqi Duan, Bo Yuan, Yuhui Shi, Jialin Liu
First submitted to arxiv on: 21 Nov 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 Medium Difficulty Summary: Large Language Models (LLMs) have made significant progress in artificial intelligence, demonstrating the ability to interact with humans and influence human cognition through information dissemination. However, recent studies have revealed instances of bias inherent within these LLMs, highlighting a critical issue that demands attention. Our research delves into the challenge of harmonizing accuracy and fairness in enhancing LLMs. While improving accuracy can enhance overall performance, it often occurs at the expense of fairness. This underscores the need to consider multiple factors during design and optimization phases. Therefore, we advocate for reformulating the LLM training process as a multi-objective learning task. Our investigation reveals that MOEL methodologies offer promising avenues for tackling this challenge. Our MOEL framework enables simultaneous optimization of accuracy and fairness metrics, resulting in a Pareto-optimal set of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: Artificial Intelligence (AI) has made great progress with Large Language Models (LLMs). However, these models can be biased, which is a big problem. Our research looks at how to make AI models fairer and better. We found that when we try to improve AI’s performance, it often becomes less fair. This means we need to consider fairness as well as performance. To fix this, we suggest changing the way AI models are trained. Our study shows that a new method called MOEL (Multi-Objective Evolutionary Learning) can help us create more fair and effective AI technologies. |
Keywords
* Artificial intelligence * Attention * Optimization
