###### Abstract  
Text classification aims to assign predefined labels to unlabeled sentences, which tend to struggle in real-world applications when only a few annotated samples are available. Previous works generally focus on using the paradigm of meta-learning to overcome the classification difficulties brought by insufficient data, where a set of auxiliary tasks is given. Accordingly, prompt-based approaches are proposed to deal with the low-resource issue. However, existing prompt-based methods mainly focus on English tasks, which generally apply English pretrained language models that can not directly adapt to Chinese tasks due to structural and grammatical differences. Thus, we propose a prompt-based Chinese text classification framework that uses generated natural language sequences as hints, which can alleviate the classification bottleneck well in low-resource scenarios. In detail, we first design a prompt-based fine-tuning together with a novel pipeline for automating prompt generation in Chinese. Then, we propose a refined strategy for dynamically and selectively incorporating demonstrations into each context. We present a systematic evaluation for analyzing few-shot performance on a wide range of Chinese text classification tasks. Our approach makes few assumptions about task resources and expertise and therefore constitutes a powerful, task-independent approach for few-shot learning. few-shot learning; prompt learning; template generation; demonstration learning +
Footnote †: journal: _aciences_  
Intuitively, the manually-designed prompt is the easiest way to elicit semantic knowledge from the language models [15; 16]. Yet, it is possible that manually created prompts are sub-optimal [17] as well as labor-intensive. In this light, prompt engineering that focuses on generating prompts automatically has been widely explored. While it is possible to obtain high-performance prompt-learning models for few-shot tasks in English, there are still many others that, due to lack of resources or attention, have not yet benefited from advances in the field of prompts. Despite the promising achievements, most existing methods only consider generating English prompts and Chinese prompt engineering methods are yet to be explored. Further, one of the best-performing training techniques is demonstration learning [18; 19], which concatenates the query with one selected example from each category for fine-tuning. Existing demonstration learning methods typically select the example at random or on the basis of similarity. However, we argue the previous demonstration learning methods are not guaranteed to prioritize the most informative example in the absence of a proper validation mechanism. In this paper, we propose a prompt-based Chinese text classification framework to solve the classification bottleneck in the true few-shot setting, i.e., a small number of training and validation samples, along with moderately-sized language models. This framework mainly consists of two novel parts, namely, the template generation module and the demonstration filtering module. In detail, we introduce an automatic prompt generation process, including a pruned brute-force search to identify the best working templates that allow us to cheaply obtain effective prompts that match or outperform our manually chosen ones. In addition, we adopt the idea of incorporating demonstrations as additional context and present an advanced candidate filtering method using mutual information and cosine similarity. Consequently, this joint correlation scoring function  
Figure 1: The example of fine-tuning, masked language modeling, and prompt-tuning.