Large Language Models Are Human-level Prompt Engineers

“Prompt Engineering Prompting offers a natural and intuitive interface for humans to interact with and use generalist models such as LLMs. Due to its flexibility, prompting has been widely used as a generic method for NLP tasks (Schick & Schütze, 2021; Brown et al., 2020; Sanh et al., 2022). However, LLMs require careful prompt engineering, either manually (Reynolds & McDonell, 2021) or automatically (Gao et al., 2021; Shin et al., 2020), as models do not seem to understand the prompts in the same way a human would (Webson & Pavlick, 2021; Lu et al., 2021). Though many successful prompt tuning methods perform optimization over a continuous space using gradient-based methods (Liu et al., 2021; Qin & Eisner, 2021; Lester et al., 2021), this becomes less practical with scale, as computing gradients becomes increasingly expensive and access to models shifts to APIs that may not provide gradient access. In our paper, we borrow components from discrete prompt search methods, such as prompt generation (Gao et al., 2021; Ben-David et al., 2021), prompt scoring (Davison et al., 2019) and prompt paraphrasing (Jiang et al., 2020; Yuan et al., 2021) to optimize instructions by searching directly in the natural language hypothesis space. As compared to this past work, which uses specialized models for each component and leans heavily on human templates, we show that the entire search can be conducted by a single LLM.

Program Synthesis

Program synthesis involves the automatic search over a “program space” to find a program satisfying a particular specification (Gulwani et al., 2017). Modern program synthesis admits a wide variety of specifications, including input-output examples (Ellis et al., 2021; Wong et al., 2021) and natural language (Jain et al., 2022). The range of feasible program spaces to search over has also grown, from historically restrictive domain-specific languages to general-purpose programming languages (Austin et al., 2021). In contrast to prior approaches that require a suitable structured hypothesis space and library of components (Liang et al., 2010; Ellis et al., 2018), we leverage the structure provided by LLMs to search over the space of natural language programs. Using inference models is a standard practice to speed up the search by restricting the search space to a limited space of possible expressions (Menon et al., 2013; Lee et al., 2018; Devlin et al., 2017; Ellis et al., 2021). Inspired by this, we use LLMs as approximate inference models to generate program candidates based on a small set of demonstrations. Unlike classical program synthesis, our inference models do not require any training and generalize well to various tasks.”