本研究探讨了大型语言模型（LLMs）在社交媒体数据中识别和注释人权侵犯的能力。通过比较不同LLMs在零样本和少样本条件下的表现，揭示了它们在处理复杂文本时的错误模式及其在多语言背景下的适用性和局限性。

本研究探讨了人工智能影响者与美国公众之间的观点差异，开发了互动平台以收集公众对人工智能的恐惧和希望。结果显示，公众对人工智能失控的恐惧明显，而影响者更关注监管，尤其在性别和种族代表性方面存在显著差异。

本研究提出了一种新方法，通过相对排名近似目标的累积分布函数，解决机器学习中多个目标的比较与聚合问题。这一方法使得原本不可比较的目标转化为可比较的，帮助实践者有效导航帕累托前沿，尤其在大语言模型选择和自动机器学习基准测试中展现潜力。

本研究探讨多模态基础模型在自主驾驶中的响应能力，特别是在分布外情境下的表现。通过Robusto-1数据集，发现人类与视觉语言模型在认知层面上的一致性与差异性与提问类型密切相关。

本研究探讨了生成性人工智能时代专业艺术家的艺术教育与人类专业技能的价值。实验表明，尽管AI在内容创作中有帮助，专业技能对输出的准确性和创造性仍至关重要。将艺术技能与AI训练结合，将为艺术家和视觉专业人士的未来发展提供新机遇。

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