Revisiting Prompt Engineering via Declarative Crowdsourcing

• URL: http://arxiv.org/abs/2308.03854v1
• Date: Mon, 7 Aug 2023 18:04:12 GMT
• Title: Revisiting Prompt Engineering via Declarative Crowdsourcing
• Authors: Aditya G. Parameswaran, Shreya Shankar, Parth Asawa, Naman Jain, Yujie Wang
• Abstract summary: Large language models (LLMs) are incredibly powerful at comprehending and generating data in the form of text, but are brittle and error-prone. We put forth a vision for declarative prompt engineering. Preliminary case studies on sorting, entity resolution, and imputation demonstrate the promise of our approach.
• Score: 16.624577543520093
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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