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Existing tools for writing prompts for language models (known as “prompt programming”) provide little support to prompt programmers. Consequently, as prompts become more complex with the addition of multiple input/output examples (“few-shot” prompts), they can be hard to read, understand, and edit. In this work, we observe that prompts are often used to solve complex problems, but lack the strict grammar of a traditional programming language. We describe methods for extracting the semantically meaningful structure of natural language prompts (e.g., regions of the prompt representing a preamble or input/output examples) in the absence of a rigid formal grammar, and demonstrate a range of editor features that can leverage this information to assist prompt programmers. Finally, we relate initial feedback from design probe explorations with a set of domain experts and provide insights to help guide the development of future prompt editors.
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Alexander J. Fiannaca
Microsoft (United States)
Chinmay Kulkarni
Microsoft (United States)
Carrie J. Cai
Vassar College
Google (United States)
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Fiannaca et al. (Wed,) studied this question.
synapsesocial.com/papers/69ec8e6858b466a3098fe561 — DOI: https://doi.org/10.1145/3544549.3585737