Continual Pre-Training of Python Language Model to mT5

PAINe: An Artificial Intelligence–based Virtual Assistant to Aid in the Differentiation of Pain of Odontogenic versus Temporomandibular Origin

Burnout has become a significant concern in high-stress industries, impacting mental health and productivity. Traditional burnout management solutions lack real-time adaptability and personalized intervention strategies. This research explores an AI-driven burnout management mechanism leveraging Generative AI, MongoDB Atlas, Python, and Large Language Models (LLMs) to provide real-time detection, personalized coping strategies, and predictive analytics. The system collects and stores user responses, behavioral patterns, and sentiment data in MongoDB Atlas, while LLMs analyze and generate tailored burnout mitigation recommendations. Experimental results demonstrate the effectiveness of the proposed approach in early burnout detection, personalized intervention, and user engagement, showing improvements in stress reduction and work-life balance. The proof of concept for AI-powered burnout detection and intervention demonstrates the technical feasibility of using Large Language Models (LLMs), Generative AI, MongoDB Atlas, and Python-based automation for real-time mental health support. The system successfully classifies burnout severity using LLMs trained on sentiment analysis, achieving an accuracy of 91.2%, which significantly outperforms traditional rule-based sentiment models. The use of MongoDB Atlas for scalable data storage ensures real-time burnout tracking, while FastAPI-based backend automation allows for seamless integration across platforms. Furthermore, Generative AI provides personalized stress interventions, dynamically adapting to user feedback and improving engagement. These technical validations highlight how AI-driven burnout management can be scalable, real-time, and enterprise-ready, paving the way for AI-powered diagnostics in mental health applications across various industries.

With the rapid advancement of large language models (LLMs), code translation has become a critical yet challenging task in software engineering. Existing evaluations of this task remain unreliable due to two key limitations: the absence of specialized function-level code translation benchmarks and flawed evaluation pipelines. Specifically, current pipelines lack post-processing mechanisms to extract target code from LLMs’ inherently unstable output formats, directly resulting in non-executable translations and false negatives in evaluation. To address these issues, we introduce FuncTransEval, an enhanced multilingual benchmark. It comprises 400×4 functions across four programming languages (Python, C++, Java, and JavaScript) and 12 bidirectional translation pairs, each paired with executable unit tests for fine-grained functional validation. Using FuncTransEval, we systematically assess LLMs’ true capabilities in code translation and identify their core limitations: inadequate understanding of the implicit information such as function logic, type specifications, and cross-language differences. To mitigate these shortcomings, we propose ICAM-Trans (Implicit-Context-Aware Multi-Agent Framework for function-level code translation). ICAM-Trans is a hierarchical, autonomous multi-agents architecture that explicitly uncovers and leverages these implicit contextual information during translation. It employs a Translation Orchestrate Agent (TOA) to autonomously coordinate four specialized Context Analysis Agents (CAAs), which conduct pre-translation analyses on different aspects. Insights from these analyses guide a Context-Aware Translation Agent (CTA) to generate semantically faithful target code. Experiments on FuncTransEval demonstrate that ICAM-Trans consistently outperforms strong baselines, validating its effectiveness in achieving high-fidelity and interpretable function-level code translation.

With the rise in using Large Language Models (LLMs), research proved the feasibility of using LLMs in various software engineering tasks including code generation. However, it remains a major challenge to ensure the correctness and the efficiency of the LLM-generated code. This paper applies the "Chain-of-thought" prompting approach to improve GPT performance in code generation and its ability to convert coding requirements from HackerRank and LeetCode online coding platforms into correct Python, C++, and JavaScript code. We evaluated the performance of GPT-3.5 and GPT-4 across 558 programming challenges on HackerRank and LeetCode platforms. Our evaluation framework employed a comprehensive test case validation system, where each generated solution was tested against predefined input-output pairs, with success determined by exact matches between expected and actual outputs. The evaluation revealed significant variations in success rates across platforms (HackerRank: 46.83% vs. LeetCode: 7.35%) and programming languages (Python leading with 28.6% overall success rate). These results demonstrate that while the chain of thought approach enhances code generation accuracy, particularly for structured programming tasks, significant challenges remain in handling complex algorithmic problems.

Artificial Intelligence (AI) driven code generation tools are increasingly used throughout the software development lifecycle to accelerate coding tasks. However, the security of AI-generated code using large language models (LLMs) remains underexplored, and recent studies have revealed various risks and weaknesses. This paper presents a measurement study of LLM-generated code across four programming languages (Python, Java, C++, and C) and five widely used LLM families. We construct a manually curated dataset of 200 programming tasks, grouped into seven functional and security-relevant categories, each with language-neutral specifications. For every combination of task, language, and model, we generate code and evaluate it along three axes: syntactic validity and compilation success, semantic correctness using 4,000 per program unit test files, and software quality and security using SonarQube and CodeQL, complemented by manual review of key static analysis findings. Our results show clear language effects: Python and Java achieve higher compilation and semantic correctness rates and produce fewer security findings than C and C++, where we observe more memory safety issues, hard-coded secrets, and cryptographic misuses. We also find that many models fail to make use of modern security features available in recent compiler and toolkit updates (i.e., in Java 17), and that outdated methods remain common, particularly in C++. These findings highlight the need to advance LLMs so that they better align with emerging secure coding practices and language-specific best practices. All code and data are available at GitHub.