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。