通过跨不同语言的大型语言模型发现新知识学习中的不平等
Chenglong Wang1,2, Haoyu Tang3, Xiyuan Yang4
1School of Urban Planning & Design, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
概括
大型语言模型 (LLM) 难以在不同语言中平等地学习新信息,在资源较低的语言中显示出挑战. 这项研究强调了在LLM开发中需要解决这些语言不平等的问题.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能伦理学 人工智能伦理学
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 对全球生产力和问题解决越来越重要.
- 现有的LLM语言不平等研究重点是静态能力.
- 法学士的动态学习和知识获取需要调查不断变化的语言差异.
研究的目的:
- 在LLMs的动态学习过程中探索语言不平等.
- 在四个方面分析这些不平等:有效性,可转移性,优先级和稳定性.
- 确定在法学士课程中出现的语言差异的原因并提出缓解策略.
主要方法:
- 使用上下文学习和微调设置进行了广泛的实验.
- 评估了专有和开源的大型语言模型.
- 分析了不同语言和关键维度的新知识获取方面的不平等现象.
主要成果:
- 在低资源语言中,LLM在学习新知识方面表现出更大的困难,以高效准确地学习新知识.
- 从资源较高的语言转移到资源较低的语言,知识转移更有效.
- 高资源语言的新知识更有可能被保留和优先考虑.
- 在高资源语言中,LLM表现出对错误信息的强度增加.
结论:
- 在LLM的动态知识获取过程中存在显著的语言不平等.
- 这些差异源于语言因素,预训练数据和tokenizer设计.
- 解决这些不平等问题,可能通过语言神经元,对于公平的LLM发展至关重要.
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