Lightweight large language english interaction model based on embedded learning terminal

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Significant performance bottlenecks exist in current embedded learning devices, which is manifested in that the limited computing resources can not support complex natural language processing tasks, causing the traditional model to face great challenges in terminal deployment. Moreover, response delay and high energy consumption also become the main constraints. Consequently, these problems have seriously affected the English learning experience. The core bottleneck lies in the fundamental conflict between rigid computational resource constraints and the required depth of semantic understanding. To address this, this study proposes a hybrid compression framework (Hybrid Compression Framework, HCF), rather than relying on any single compression technique. The key innovation is not a simple integration of existing methods like knowledge distillation (KD) or neural architecture search (NAS). Instead, it lies in the co-design of two novel modules: (i) a cognitive oriented sparse constraint (Cognitive Oriented Sparse Constraint, COSC), which dynamically protects critical parameters via dependency parsing, and (ii) an incremental semantic calibration mechanism (Incremental Semantic Calibration Mechanism, ISCM), which controls semantic drift through memory replay. Their synergy achieves 92.3% semantic accuracy under a 1.5W power budget. This resolves the long-standing trade-off between semantic fidelity and computational efficiency under extreme resource limitation. Additionally, the multi-source training strategy strengthens the generalization ability of the model. The comprehensive experimental analysis indicates that in the reasoning efficiency test, the average response time is 187 ms, with the semantic accuracy maintained at 92.3%. Compared with the current mainstream method, the scene response time is reduced by 25%, with the memory efficiency improved by 30% and the energy consumption reduced by 23%, which has significantly improved the system performance. The improvements have clear application values, promote the technological innovation of mobile learning terminals, as well as the development process of personalized education.