Abstract Recent advances in artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) have led to the development of a new generation of Large Language Models (LLMs) trained on massive amounts of data. Commercial applications (e.g., ChatGPT) have made this available to the general public, enabling the use of LLMs to produce high-quality texts for academic and professional purposes. Educational institutions are increasingly aware of students’ use of AI-generated content and are researching its impact and potential misuse. Computer Science (CS) and related fields are particularly affected, as LLMs can also generate programming code in various languages. To understand the potential impact of publicly available LLMs in CS education, we extend our previously introduced (Raihan et al. 2024), a framework comprising hundreds of programming exercise prompts and multiple-choice questions from introductory CS and programming courses. We provide experimental results on , evaluating the performance of several LLMs in generating Python code and answering basic computer science and programming questions, offering insights into the implications of this technology for CS education.

Language transfer is an important topic of research in second language acquisition and computational linguistics.The availability of suitable learner corpora is paramount for the study of second language acquisition (SLA) and language transfer.However, curating learner corpora is a challenging endeavor as high quality learner data is rarely publicly available.This results in only a few such corpora available to the community.To address this important gap, in this paper we present LENS, a novel English learner corpus with longitudinal data which enables researchers to investigate language learning over time.LENS contains 687 instances written by speakers of 15 different L1s.We use LENS two perform two important tasks at the intersection of SLA and Computational Linguistics: (1) Native Language Identification (NLI); and (2) an evaluation of large language models as a tool for high-precision, semi-automated annotation of L1 interference features.1

The proliferation of propaganda on mobile platforms raises critical concerns around detection accuracy and user privacy. To address this, we propose TRIDENT -a three-tier propaganda detection model implementing transformers, adversarial learning, and differential privacy which integrates syntactic obfuscation and label perturbation to mitigate privacy leakage while maintaining propaganda detection accuracy. TRIDENT leverages multilingual back-translation to introduce semantic variance, character-level noise, and entity obfuscation for differential privacy enforcement, and combines these techniques into a unified defense mechanism. Using a binary propaganda classification dataset, baseline transformer models (BERT, GPT-2) we achieved F1 scores of 0.89 and 0.90. Applying TRIDENT's third-tier defense yields a reduced but effective cumulative F1 of 0.83, demonstrating strong privacy protection across mobile ML deployments with minimal degradation.

Dhiman Goswami, Sharanya Thilagan, Kai North, Shervin Malmasi, Marcos Zampieri. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.