The extraction of a small number of relevant insights from vast amounts of
data is a crucial component of data-driven decision-making. However,
accomplishing this task requires considerable technical skills, domain
expertise, and human labor. This study explores the potential of using Large
Language Models (LLMs) to automate the discovery of insights in data,
leveraging recent advances in reasoning and code generation techniques. We
propose a new evaluation methodology based on a "capture the flag" principle,
measuring the ability of such models to recognize meaningful and pertinent
information (flags) in a dataset. We further propose two proof-of-concept
agents, with different inner workings, and compare their ability to capture
such flags in a real-world sales dataset. While the work reported here is
preliminary, our results are sufficiently interesting to mandate future
exploration by the community.

利用大型语言模型自动发现数据中的洞察力并评估其能力的研究。

征服旗帜：利用大型语言模型揭示数据洞察

Capture the Flag: Uncovering Data Insights with Large Language Models

In this paper, we carefully investigate how we can use multiple different
Natural Language Processing techniques and methods in order to automatically
recognize the main actions in sports events. We aim to extract insights by
analyzing live sport commentaries from different sources and by classifying
these major actions into different categories. We also study if sentiment
analysis could help detect these main actions.

我们研究了如何使用多种自然语言处理技术和方法自动识别体育赛事中的主要动作，并通过分析来自不同来源的现场体育评论以及将这些主要动作分类来提取洞察。我们还研究了情感分析是否能够帮助检测这些主要动作。

分析体育评论以自动识别事件并提取观点

Analyzing sports commentary in order to automatically recognize events  and extract insights

We describe Voyageur, which is an application of experiential search to the
domain of travel. Unlike traditional search engines for online services,
experiential search focuses on the experiential aspects of the service under
consideration. In particular, Voyageur needs to handle queries for subjective
aspects of the service (e.g., quiet hotel, friendly staff) and combine these
with objective attributes, such as price and location. Voyageur also highlights
interesting facts and tips about the services the user is considering to
provide them with further insights into their choices.

Voyageur 是一款应用于旅游领域的经验搜索应用程序，与传统搜索引擎不同，它专注于考虑服务的经验性方面，需要处理主观查询方面的服务，而这些服务与价格、位置等客观属性相结合，同时提供有关所考虑服务的有趣事实和提示，以便为用户提供更多的洞见。

Voyageur: 一款体验式旅游搜索引擎

Voyageur: An Experiential Travel Search Engine

Statistical learning theory provides the theoretical basis for many of
today's machine learning algorithms. In this article we attempt to give a
gentle, non-technical overview over the key ideas and insights of statistical
learning theory. We target at a broad audience, not necessarily machine
learning researchers. This paper can serve as a starting point for people who
want to get an overview on the field before diving into technical details.

这篇研究论文对统计学习理论的主要想法和见解进行了温和、非技术性的概述。这篇论文可作为人们在深入了解技术细节之前对该领域有一个概要的起始点。