HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models

By Swaroop Mishra, Elnaz Nouri. 2022

33 citations recorded by the source corpus.

Controlling the text generated by language models and customizing the content has been a long-standing challenge. Existing prompting techniques proposed in pursuit of providing control are task-specific and lack generality; this provides overwhelming choices for non-expert users to find a suitable method for their task. The effort associated with those techniques, such as in writing examples, explanations, instructions, etc. further limits their adoption among non-expert users. In this paper, we propose a simple prompting strategy HELP ME THINK where we encourage GPT3 to help non-expert users by asking a set of relevant questions and leveraging user answers to execute the task. We demonstrate the efficacy of our technique HELP ME THINK on a variety of tasks. Specifically, we focus on tasks that are hard for average humans and require significant thinking to perform. We hope our work will encourage the development of unconventional ways to harness the power of large language models.

Subfields: Artificial Intelligence, Prompting & Reasoning

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Citations in the corpus

Augmented Language Models: a Survey (2023, background): “, 2019), or solve the sub-problems sequentially, where the solution to the next sub-problem depends on the answer to the previous ones (Yang et al., 2022a; Zhou et al., 2022; Drozdov et al., 2022; Dua et al., 2022; Khot et al., 2022; Wang et al., 2022a; Wu et al., 2022b; Mishra and Nouri, 2022).”
How Many Data Samples is an Additional Instruction Worth? (2022, methodology): “In addition, many approaches have been proposed to improve model performance using instructions (Wu et al., 2022; Lin et al., 2021; Wang et al., 2022b; Luo et al., 2022; Kuznia et al., 2022; Patel et al., 2022; Mishra and Nouri, 2022).”
Help me write a Poem - Instruction Tuning as a Vehicle for Collaborative Poetry Writing (2022, methodology): “To enable richer interaction, we rely on instructional prompts (Wang et al., 2022; Sanh et al., 2021; Mishra et al., 2022; Mishra and Nouri, 2022) that act as a natural language interface between the user and the assistant.”
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias (2023, background): “Several works attempt to optimize discrete prompts for querying LLMs with large language models [43, 64, 34].”