@inproceedings{barke-etal-2024-solving,
title = "Solving Data-centric Tasks using Large Language Models",
author = "Barke, Shraddha and
Poelitz, Christian and
Negreanu, Carina and
Zorn, Benjamin and
Cambronero, Jos{\'e} and
Gordon, Andrew and
Le, Vu and
Nouri, Elnaz and
Polikarpova, Nadia and
Sarkar, Advait and
Slininger, Brian and
Toronto, Neil and
Williams, Jack",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.41",
pages = "626--638",
abstract = "Large language models are rapidly replacing help forums like StackOverflow, and are especially helpful to non-professional programmers and end users. These users are often interested in \textit{data-centric tasks}, like spreadsheet manipulation and data wrangling, which are hard to solve if the intent is only communicated using a natural-language description, without including 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 novel \textit{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.",
}
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<abstract>Large language models are rapidly replacing help forums like StackOverflow, and are especially helpful to non-professional programmers and end users. These users are often interested in data-centric tasks, like spreadsheet manipulation and data wrangling, which are hard to solve if the intent is only communicated using a natural-language description, without including 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 novel 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.</abstract>
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%0 Conference Proceedings
%T Solving Data-centric Tasks using Large Language Models
%A Barke, Shraddha
%A Poelitz, Christian
%A Negreanu, Carina
%A Zorn, Benjamin
%A Cambronero, José
%A Gordon, Andrew
%A Le, Vu
%A Nouri, Elnaz
%A Polikarpova, Nadia
%A Sarkar, Advait
%A Slininger, Brian
%A Toronto, Neil
%A Williams, Jack
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F barke-etal-2024-solving
%X Large language models are rapidly replacing help forums like StackOverflow, and are especially helpful to non-professional programmers and end users. These users are often interested in data-centric tasks, like spreadsheet manipulation and data wrangling, which are hard to solve if the intent is only communicated using a natural-language description, without including 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 novel 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.
%U https://aclanthology.org/2024.findings-naacl.41
%P 626-638
Markdown (Informal)
[Solving Data-centric Tasks using Large Language Models](https://aclanthology.org/2024.findings-naacl.41) (Barke et al., Findings 2024)
ACL
- Shraddha Barke, Christian Poelitz, Carina Negreanu, Benjamin Zorn, José Cambronero, Andrew Gordon, Vu Le, Elnaz Nouri, Nadia Polikarpova, Advait Sarkar, Brian Slininger, Neil Toronto, and Jack Williams. 2024. Solving Data-centric Tasks using Large Language Models. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 626–638, Mexico City, Mexico. Association for Computational Linguistics.