# TwinStar: A Novel Design for Enhanced Test Question Generation Using Dual-LLM Engine
## 2 Background and Related Work
### Question Generation Approaches  
Prior research on question generation has typically taken one of the following two approaches: (1) rule-based methods [22; 23; 24] and (2) neural network-based methods [25; 26; 27]. Some studies have achieved basic levels of automation in question generation using templates. For instance, [PERSON] et al. [28] generated mathematical word problems based on ontological predicates. [PERSON] et al. [29] proposed an automatic template extraction method, while [PERSON] et al. [23] and [PERSON] et al. [22] developed semi-automatic template extraction methods. However, these methods heavily depend on designers' expertise in crafting templates, often resulting in rigid and repetitive question formats.  
In recent years, advancements in sequence-to-sequence models, such as Recurrent Neural Networks (RNNs) and Transformers, have inspired extensive research into neural network-based question generation. For example, [PERSON] et al. [26] employed a Long Short-Term Memory (LSTM)-based model to produce elementary mathematical word problems, combining character, word, and part-of-speech tag embeddings. Similarly, [PERSON] et al. [25] utilized a Transformer-based architecture, where the encoder extracted topic and mathematical formula features and the decoder generated mathematical word problems. Pre-trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and the Generative Pre-trained Transformer (GPT) series, have demonstrated robust performance across a range of tasks. Studies suggest that questions generated by the GPT series often outperform those from BERT-based models [30].  
However, current techniques for question generation predominantly focus on mathematical word problems and reading comprehension, limiting the range of cognitive levels they address. Specifically, mathematical word problems often assess students' application skills within [PERSON]'s taxonomy. In practical educational assessments, questions must evaluate multiple cognitive levels for specific knowledge points. Yet, existing research lacks a clear focus on generating multilevel cognitive questions tied to specific knowledge points. Consequently, prior studies offer no direct solutions to the challenges addressed in this paper.  
Recent advancements in large-scale language models have created new opportunities for educational question generation. Although researchers have explored the use of ChatGPT-like models to label educational materials or assist teachers in creating them, their effectiveness remains largely unvalidated. [PERSON] et al. [31] examined the effectiveness of large language models in generating educational questions and found that the generated questions were generally high-quality and suitable for classroom use, though their alignment with [PERSON]'s cognitive levels was relatively low. Other studies have attempted to generate domain-specific questions by training pre-trained models from scratch, followed by fine-tuning [20]. However, these efforts primarily evaluate question quality based on grammatical correctness without considering multilevel cognitive assessments. Moreover, these approaches often require substantial domain-specific data, which are scarce in real-world settings. Enhancing a language model's question generation capabilities for specialized domains with limited data remains an open challenge.