从混合信息来源中学习
Learning from a Mixture of Information Sources
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中文总结 AI 辅助
本文扩展布莱克威尔实验模型，研究信息来源分布对学习的影响，发现来源感知决策者偏好均值保留展形，而来源盲决策者可通过至多O(1/ε)倍数据达到相同学习效果。
中文摘要 AI 辅助
我们常常从以不同方式传达信息的多个来源中学习。知道信号来源的信息量有多大，以及这种信息量如何受来源分布的影响？我们通过引入一个关于信号机制的常见已知分布（代表信息来源的分布），扩展了标准的（二元状态、二元信号）布莱克威尔实验模型。我们比较了两种信息模型下的学习：来源感知模型，其中决策者观察信号机制及其实现（例如，原始评论、搜索结果）；以及来源盲模型，其中仅观察信号实现（例如，聚合评分、LLM生成的摘要）。我们表明，信号机制分布中的均值保留展形转化为来源感知决策者的布莱克威尔优势，意味着他们在信息来源上是“风险偏好”的。相比之下，这对来源盲决策者没有影响。当从信号机制的重复抽取中学习时，来源盲决策者学习得更慢。然而，只要平均信号机制距离完全无信息有$\varepsilon$的距离，来源盲学习可以通过使用最多$O(1/\varepsilon)$倍的数据来匹配来源感知学习。
英文摘要
We often learn from multiple sources that convey information in different ways. How informative is it to know the source of a signal, and how is this informativeness shaped by the distribution of sources? We extend the standard (binary-state, binary-signal) Blackwell experiment model by introducing a commonly known distribution over signaling schemes, representing the distribution of information sources. We compare learning under two information models: source-aware, where decision makers observe a signaling scheme and its realization (e.g., raw reviews, search results), and source-blind, where only the signal realization is observed (e.g., aggregate ratings, LLM-generated summaries). We show that a mean-preserving spread in the distribution of signaling schemes translates into Blackwell dominance for source-aware decision makers, implying they are "risk-loving" in information sources. In contrast, it has no impact on source-blind decision makers. When learning from repeated draws of signaling schemes, source-blind decision makers learn more slowly. However, as long as the average signaling scheme is $\varepsilon$ away from being completely uninformative, source-blind learning can match source-aware learning by using at most $O(1/\varepsilon)$ times more data.
发表机构
- Microsoft Research（微软研究院）
- Cornell University（康奈尔大学）
- Rensselaer Polytechnic Institute（伦斯勒理工学院）
- Amazon（亚马逊）
机构由 AI 辅助整理，请以论文原文为准。
