Magic Mirror on the Wall, Which Is the Fairest Prompt of All? A Survey on Automatic Prompt Learning

Stefano Fantazzini, Giacomo Frisoni, Gianluca Moro, Luca Ragazzi, Mario Ciccioni, Claudio Sartori

Abstract: Prompts direct the behavior of a model by conditioning its outputs on carefully designed instructions and examples, similar to setting the trajectory of an arrow before release. More broadly, prompt learning is the research area that aims to solve downstream tasks by directly leveraging the knowledge acquired by language models at pretraining time, removing the need for expensive fine-tuning stages with potentially different objective functions. While manual prompt engineering has enabled both small and large language models to achieve superhuman performance on numerous benchmarks, it remains a labor-intensive and suboptimal process. Recently, the field has shifted towards automating the search for prompts that effectively elicit the desired model responses. This survey presents the first systematic review of prompt learning for pre-trained language models operating on textual inputs, with a particular focus on automatic methods. We critically analyze existing publications and organize them into a novel taxonomy, describing key aspects for practical usage. We finally discuss promising directions for future research. Our curated repository of annotated papers, continuously updated, is available at https://github.com/disi-unibo-nlp/awesome-prompt-learning.

External IDs:doi:10.3233/faia251343