用多目标优化平衡大模型的准确率与公平性，避免偏见。
Exploring Accuracy-Fairness Trade-off in Large Language Models
- 将训练设为多目标优化问题，同时提升准确率与公平性。
- 在多个数据集上实现准确率与公平性的帕累托最优平衡。
- 适合关注AI伦理、模型公正性的研究者和开发者。
大语言模型（LLMs）在人工智能领域取得显著进展，具备与人类交互并影响认知的能力。然而，近期研究揭示了其内在偏见问题，亟需关注。本文深入探讨提升LLM性能时准确率与公平性之间的权衡。单纯追求准确率往往牺牲公平性，反之亦然。因此，我们主张将训练过程重构为多目标学习任务。研究表明，多目标进化学习（MOEL）方法能有效解决该挑战，实现准确率与公平性的同时优化，生成帕累托最优模型集合。本研究为真实场景中更公平、高效的AI技术提供了可行路径。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have made significant strides in the field of artificial intelligence, showcasing their ability to interact with humans and influence human cognition through information dissemination. However, recent studies have brought to light instances of bias inherent within these LLMs, presenting a critical issue that demands attention. In our research, we delve deeper into the intricate challenge of harmonising accuracy and fairness in the enhancement of LLMs. While improving accuracy can indeed enhance overall LLM performance, it often occurs at the expense of fairness. Overemphasising optimisation of one metric invariably leads to a significant degradation of the other. This underscores the necessity of taking into account multiple considerations during the design and optimisation phases of LLMs. Therefore, we advocate for reformulating the LLM training process as a multi-objective learning task. Our investigation reveals that multi-objective evolutionary learning (MOEL) methodologies offer promising avenues for tackling this challenge. Our MOEL framework enables the simultaneous optimisation of both accuracy and fairness metrics, resulting in a Pareto-optimal set of LLMs. In summary, our study sheds valuable lights on the delicate equilibrium between accuracy and fairness within LLMs, which is increasingly significant for their real-world applications. By harnessing MOEL, we present a promising pathway towards fairer and more efficacious AI technologies.
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