Large language models (LLMs) are rapidly replacing help forums like
StackOverflow, and are especially helpful for non-professional programmers and
end users. These users are often interested in data-centric tasks, such as
spreadsheet manipulation and data wrangling, which are hard to solve if the
intent is only communicated using a natural-language description, without
including the data. But how do we decide how much data and which data to
include in the prompt? This paper makes two contributions towards answering
this question. First, we create a dataset of real-world NL-to-code tasks
manipulating tabular data, mined from StackOverflow posts. Second, we introduce
a cluster-then-select prompting technique, which adds the most representative
rows from the input data to the LLM prompt. Our experiments show that LLM
performance is indeed sensitive to the amount of data passed in the prompt, and
that for tasks with a lot of syntactic variation in the input table, our
cluster-then-select technique outperforms a random selection baseline.

通过挖掘 StackOverflow 帖子中的实际自然语言到代码任务来创建一个数据集，该论文提出了一种以聚类选择为基础的提示技术来确定在 LLMs 提示中包含多少数据以及选择哪些数据，并通过实验表明 LLM 的性能确实对提示中所传递的数据量敏感，对于输入表中存在大量语法变化的任务，聚类选择技术优于随机选择基准模型。

利用大型语言模型解决数据中心任务

Solving Data-centric Tasks using Large Language Models

Generative AI workflows heavily rely on data-centric tasks - such as
filtering samples by annotation fields, vector distances, or scores produced by
custom classifiers. At the same time, computer vision datasets are quickly
approaching petabyte volumes, rendering data wrangling difficult. In addition,
the iterative nature of data preparation necessitates robust dataset sharing
and versioning mechanisms, both of which are hard to implement ad-hoc. To solve
these challenges, we propose a "dataset factory" approach that separates the
storage and processing of samples from metadata and enables data-centric
operations at scale for machine learning teams and individual researchers.

利用数据集工厂方法实现数据集存储和处理的分离，以及在机器学习团队和个人研究者间实现大规模数据中心化操作，以解决生成式人工智能工作流中数据处理和共享的挑战。

数据集工厂：用于生成计算机视觉数据集的工具链

Dataset Factory: A Toolchain For Generative Computer Vision Datasets

The past decade has witnessed a plethora of works that leverage the power of
visualization (VIS) to interpret machine learning (ML) models. The
corresponding research topic, VIS4ML, keeps growing at a fast pace. To better
organize the enormous works and shed light on the developing trend of VIS4ML,
we provide a systematic review of these works through this survey. Since data
quality greatly impacts the performance of ML models, our survey focuses
specifically on summarizing VIS4ML works from the data perspective. First, we
categorize the common data handled by ML models into five types, explain the
unique features of each type, and highlight the corresponding ML models that
are good at learning from them. Second, from the large number of VIS4ML works,
we tease out six tasks that operate on these types of data (i.e., data-centric
tasks) at different stages of the ML pipeline to understand, diagnose, and
refine ML models. Lastly, by studying the distribution of 143 surveyed papers
across the five data types, six data-centric tasks, and their intersections, we
analyze the prospective research directions and envision future research
trends.

本论文对机器学习模型的数据方面的可视化相关研究进行了系统梳理和分析，提出了五类数据类型和六种数据中心任务，并分析了 143 篇论文的相关分布，展望了未来的研究趋势和方向。