The springing up of large language models (LLMs) has shifted the community from single-task-orientated natural language processing (NLP) research to a holistic end-to-end multi-task learning paradigm. Along this line of research endeavors in the area, LLM-based prompting methods have attracted much attention, partially due to the technological advantages brought by prompt engineering (PE) as well as the underlying NLP principles disclosed by various prompting methods. Traditional supervised learning usually requires training a model based on labeled data and then making predictions. In contrast, PE methods directly use the powerful capabilities of existing LLMs (e.g., GPT-3 and GPT-4) via composing appropriate prompts, especially under few-shot or zero-shot scenarios. Facing the abundance of studies related to the prompting and the ever-evolving nature of this field, this article aims to 1) illustrate a novel perspective to review existing PE methods within the well-established communication theory framework, 2) facilitate a better/deeper understanding of developing trends of existing PE methods used in three typical tasks, and 3) shed light on promising research directions for future PE methods.

Recurrent neural network transducer (RNN-T) is an important branch of current end-to-end automatic speech recognition (ASR). Various promising approaches have been designed for boosting RNN-T architecture; however, few studies exploit the effectiveness of pretrained methods in this framework. In this paper, we introduce the pretrained acoustic extractor (PAE) and the pretrained linguistic network (PLN) to enhance the Conformer long short-term memory (Conformer-LSTM) transducer. First, we construct the input of the acoustic encoder with two different latent representations: one extracted by PAE from the raw waveform, and the other obtained from filter-bank transformation. Second, we fuse an extra semantic feature from the PLN into the joint network to reduce illogical and homophonic errors. Compared with previous works, our approaches are able to obtain pretrained representations for better model generalization. Evaluation on two large-scale datasets has demonstrated that our proposed approaches yield better performance than existing approaches.