基于纳什学习与自适应反馈的语言对齐
Language Alignment via Nash-learning and Adaptive feedback
AI总结：
该研究提出LANA算法，将大语言模型对齐建模为针对自适应反馈的镜像下降博弈，无需人工标注偏好数据集即可实现自对齐，并通过实验和数学分析验证了有效性。
摘要：
近期研究表明，通过在极小极大博弈框架中引入偏好模型的概念，基于人类反馈的纳什学习在大语言模型对齐方面展现出潜力。我们进一步推进这一思路，将对齐问题建模为针对改进对手自适应反馈的镜像下降算法，从而完全无需学习偏好模型，也无需标注数据集。由此得到的算法我们称为基于纳什学习与自适应反馈的语言对齐（LANA），能够在无需人工标注偏好数据集的情况下实现自对齐。我们通过多项实验和数学推导验证了这一结论。
英文摘要：
Recent research has shown the potential of Nash Learning via Human Feedback for large language model alignment by incorporating the notion of a preference model in a minimax game setup. We take this idea further by casting the alignment as a mirror descent algorithm against the adaptive feedback of an improved opponent, thereby removing the need for learning a preference model or the existence of an annotated dataset altogether. The resulting algorithm, which we refer to as Language Alignment via Nash-learning and Adaptive feedback (LANA), is capable of self-alignment without the need for a human-annotated preference dataset. We support this statement with various experiments and mathematical discussion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
