How Humans Communicate Programming Tasks in Natural Language and Implications For End-User Programming with LLMs

要旨

Large language models (LLMs) like GPT-4 can convert natural-language descriptions of a task into computer code, making them a promising interface for end-user programming. We undertake a systematic analysis of how people with and without programming experience describe information-processing tasks (IPTs) in natural language, focusing on the characteristics of successful communication. Across two online between-subjects studies, we paired crowdworkers either with one another or with an LLM, asking senders (always humans) to communicate IPTs in natural language to their receiver (either a human or LLM). Both senders and receivers tried to answer test cases, the latter based on their sender's description. While participants with programming experience tended to communicate IPTs more successfully than non-programmers, this advantage was not overwhelming. Furthermore, a user interface that solicited example test cases from senders often, but not always, improved IPT communication. Allowing receivers to request clarification, though, was less successful at improving communication.

著者
Madison Pickering
University of Chicago, Chicago, Illinois, United States
Helena Williams
University of Chicago, Chicago, Illinois, United States
Alison Gan
University of Chicago, Chicago, Illinois, United States
Weijia He
University of Southampton, Southampton, United Kingdom
Hyojae Park
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Francisco Piedrahita Velez
Brown University, Providence, Rhode Island, United States
Michael L.. Littman
Brown University, Providence, Rhode Island, United States
Blase Ur
University of Chicago, Chicago, Illinois, United States
DOI

10.1145/3706598.3713271

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713271

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Programming and Interaction

G304
7 件の発表
2025-05-01 18:00:00
2025-05-01 19:30:00
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