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The burgeoning sophistication of Artificial Intelligence (AI) has catalyzed the rapid proliferation of Large Language Models (LLMs) within software development. These models are increasingly employed to automate the generation of functionally correct code, address complex computational problems, and facilitate the debugging of existing software systems. However, LLM-generated code often faces challenges due to inherent inefficiencies, including redundant logical structures, factually inconsistent content (hallucinations), and programming errors. To address this issue, our research rigorously evaluated the computational efficiency of Python code generated by three prominent LLMs: GPT-4o-Mini, GPT-3.5-Turbo, and GPT-4-Turbo. The evaluation metrics encompass execution time, memory utilization, and peak memory consumption, while maintaining the functional correctness of the generated code. Leveraging the EffiBench benchmark datasets within the Google Vertex AI Workbench environment, across a spectrum of machine configurations, the study implemented a consistent seed parameter to ensure experimental reproducibility. Furthermore, we investigated the impact of two distinct optimization strategies: Chain-of-Thought (CoT) prompting and model fine-tuning. Our findings reveal a significant enhancement in efficiency metrics for GPT-4o-Mini and GPT-3.5-Turbo when employing CoT prompting; however, this trend was not observed for GPT-4-Turbo. Based on its promising performance with CoT prompting, we selected the GPT-4o-Mini model for subsequent fine-tuning, aiming to further enhance both its computational efficiency and accuracy. However, contrary to our expectations, fine-tuning the GPT-4o-Mini model led to a discernible degradation in both its accuracy and computational efficiency. In conclusion, this study provides empirical evidence suggesting that the deployment of high-CPU machine configurations, in synergy with the utilization of the GPT-4o-Mini model and CoT prompting techniques, yields demonstrably more efficient and accurate LLM-generated Python code, particularly within computationally intensive application scenarios. 

The recent rapid development in Natural Language Processing (NLP) has greatly en- hanced the effectiveness of Intelligent Tutoring Systems (ITS) as tools for healthcare education. These systems hold the potential to improve health-related quality of life (HRQoL) outcomes, especially for populations with limited English reading and writing skills. However, despite the progress in pre-trained multilingual NLP models, there exists a noticeable research gap when it comes to code-switching within the medical context. Code-switching is a prevalent phenomenon in multilingual communities where individuals seamlessly transition between languages during conversations. This presents a distinctive challenge for healthcare ITS aimed at serving multilin- gual communities, as it demands a thorough understanding of and accurate adaptation to code- switching, which has thus far received limited attention in research. The hypothesis of our work asserts that the development of an ITS for healthcare education, culturally appropriate to the Hispanic population with frequent code-switching practices, is both achievable and pragmatic. Given that text classification is a core problem to many tasks in ITS, like sentiment analysis, topic classification, and smart replies, we target text classification as the application domain to validate our hypothesis. Our model relies on pre-trained word embeddings to offer rich representations for understand- ing code-switching medical contexts. However, training such word embeddings, especially within the medical domain, poses a significant challenge due to limited training corpora. In our approach to address this challenge, we identify distinct English and Spanish embeddings, each trained on medical corpora, and subsequently merge them into a unified vector space via space transforma- tion. In our study, we demonstrate that singular value decomposition (SVD) can be used to learn a linear transformation (a matrix), which aligns monolingual vectors from two languages in a single meta-embedding. As an example, we assessed the similarity between the words “cat” and “gato” both before and after alignment, utilizing the cosine similarity metric. Prior to alignment, these words exhibited a similarity score of 0.52, whereas after alignment, the similarity score increased to 0.64. This example illustrates that aligning the word vectors in a meta-embedding enhances the similarity between these words, which share the same meaning in their respective languages. To assess the quality of the representations in our meta-embedding in the context of code-switching, we employed a neural network to conduct text classification tasks on code-switching datasets. Our results demonstrate that, compared to pre-trained multilingual models, our model can achieve high performance in text classification tasks while utilizing significantly fewer parameters.