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arXiv · 2308.03854

Revisiting Prompt Engineering via Declarative Crowdsourcing

Aditya G. Parameswaran·Shreya Shankar·Parth Asawa·Naman Jain·Yujie Wang
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Abstract

Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone. There has been an advent of toolkits and recipes centered around so-called prompt engineering-the process of asking an LLM to do something via a series of prompts. However, for LLM-powered data processing workflows, in particular, optimizing for quality, while keeping cost bounded, is a tedious, manual process. We put forth a vision for declarative prompt engineering. We view LLMs like crowd workers and leverage ideas from the declarative crowdsourcing literature-including leveraging multiple prompting strategies, ensuring internal consistency, and exploring hybrid-LLM-non-LLM approaches-to make prompt engineering a more principled process. Preliminary case studies on sorting, entity resolution, and imputation demonstrate the promise of our approach

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Aditya G. Parameswaran, Shreya Shankar, Parth Asawa, Naman Jain, Yujie Wang. 2023-08-07. Revisiting Prompt Engineering via Declarative Crowdsourcing. https://arxiv.org/abs/2308.03854

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