## 1 Introduction  
Requirements elicitation is the initial phase in requirements engineering and plays a crucial role in facilitating requirements evolution. This process involves gathering requirements information from stakeholders to build consensus, ensuring that software products meet user needs and expectations. It also enhances the quality of software products while reducing development costs and risks [1]. With the growing popularity of mobile smart devices, users are increasingly sharing their experiences, suggestions, and grievances about app products through reviews and star ratings on app stores [2]. Practitioners and researchers have found that summarizing and analyzing app reviews provide a more timely and accurate strategy for requirementsevolution, and automated requirements elicitation from app reviews has become a research hotspot [3; 4; 5]. Aspect-based sentiment analysis (ABSA) [6; 7] is a natural language processing (NLP) technique that has been proven effective in automatically extracting potential evolving requirements from app reviews [5]. As shown in Figure 1, the ABSA process involves two key tasks: aspect extraction and aspect sentiment classification. Initially, aspect entities \(\mathbf{a}_{i}\) and categories are extracted from the reviews (e.g., interface, bugs, button). Secondly, sentiments \(\mathbf{S}_{i}\) are categorized based on the opinions \(\mathbf{o}_{i}\) corresponding to these aspects. Finally, integrating aspect categories and sentiments allows the mapping of user feedback into actionable insights and extracting potential evolving requirements \(\mathbf{R}_{i}\). [PERSON] et al. [8] introduced the only publicly available dataset to date in this field and proposed a machine learning (ML) baseline. [PERSON] et al. [9] demonstrated that pre-trained language models (PLMs), particularly Bidirectional Encoder Representations from Transformers (BERT) and its variants, achieve highly competitive performance after fine-tuning (FT). Then, [PERSON] et al. [10] conducted in-depth exploration based on prior research and found that both the logistic regression model and FT-based BERT model achieved excellent performance. FT methods involve adjusting the entire network parameters to fit specific tasks, relying heavily on ample and diverse datasets for effective retraining to capture subtle contextual differences. However, the low-resource and high-noise characteristics of app reviews may pose challenges to the predictive accuracy, generalization, and robustness of PLMs. Recent research in the field of ABSA has increasingly focused on prompt-tuning (PT)-based masked language models (MLMs). The PT+PLMs paradigm [11] rephrases inputs in downstream tasks using carefully designed prompt templates, aligning them closely with the intended tuning objectives. PT methods effectively address challenges posed by low-resource scenarios [12; 13]. Two common strategies in PT are Hard-prompt and Soft-prompt. Soft-prompt involves inserting learnable virtual tokens into model inputs, with soft templates trained as part of the model parameters. This method is highly flexible and adaptive, automatically adjusting based on task-specific data variations to maximize model performance [14; 15]. However, dynamic prompts can lead to overfitting in noisy scenarios, reducing generalization and robustness. Additionally, Soft-prompt is often limited by model embedding dimensions and representation spaces, leading to low transferability between models and increasing potential costs in industrial applications. Hard-prompt involves carefully designed natural language words or phrases added to the model input. These prompts are not part of the model training parameters and remain constant during  
Figure 1: An example of aspect-based sentiment analysis for evolving requirements elicitation. training [13; 16]. The static nature of Hard-prompt templates offers high interpretability and stability in low-resource and noisy scenarios, though it lacks the capacity for dynamic optimization based on task characteristics.