Large language models exhibit superior capabilities in processing and understanding language, yet their applications in educational contexts remain underexplored. Learnersourcing enhances learning by engaging students in creating their own educational content. When learnersourcing multiple-choice questions, creating explanations for the solution of a question is a crucial step; it helps other students understand the solution and promotes a deeper understanding of related concepts. However, it is often difficult for students to craft effective solution explanations, due to limited subject understanding. To help scaffold the task of automated explanation generation, we present and evaluate a framework called "ILearner-LLM", that iteratively enhances the generated explanations for the given questions with large language models. Comprising an explanation generation model and an explanation evaluation model, the framework generates high-quality student-aligned explanations by iteratively feeding the quality rating score from the evaluation model back into the instruction prompt of the explanation generation model. Experimental results demonstrate the effectiveness of our ILearner-LLM on LLaMA2-13B and GPT-4 to generate higher quality explanations that are closer to those written by students on five PeerWise datasets. Our findings represent a promising path to enrich the learnersourcing experience for students and to enhance the capabilities of large language models for educational applications.

Combining large language models with logical reasoning enhance their capacity to address problems in a robust and reliable manner. Nevertheless, the intricate nature of logical reasoning poses challenges to gathering reliable data from web for building comprehensive training datasets, subsequently affecting the performance on downstream tasks. To address this, we introduce a novel logic-driven data augmentation approach, AMR-LDA. AMR-LDA converts the original text into an Abstract Meaning Representation (AMR) graph, a structured semantic representation that encapsulates the logic structure of the sentence, upon which operations are performed to generate logically modified AMR graphs. The modified AMR graphs are subsequently converted back into texts to create augmented data. Notably, our methodology is architecture-agnostic and enhances generative large language models, such as GPT-3.5 and GPT-4, through prompt augmentation, and fine-tuning discriminative large language models through contrastive learning with logic-driven data augmentation. Empirical evidence underscores the efficacy of our proposed method with improvement in performance across seven downstream tasks, such as logical reasoning reading comprehension, textual entailment, and natural language inference. Furthermore, our method ranked first on the ReClor leaderboard \url{https://eval.ai/web/challenges/challenge-page/503/leaderboard/1347}. The source code and data are publicly available \url{https://github.com/Strong-AI-Lab/Logical-Equivalence-driven-AMR-Data-Augmentation-for-Representation-Learning}.

Recent advances in artificial intelligence, including the development of highly sophisticated large language models (LLM), have proven beneficial in many real-world applications. However, evidence of inherent bias encoded in these LLMs has raised concerns about equity. In response, there has been an increase in research dealing with bias, including studies focusing on quantifying bias and developing debiasing techniques. Benchmark bias datasets have also been developed for binary gender classification and ethical/racial considerations, focusing predominantly on American demographics. However, there is minimal research in understanding and quantifying bias related to under-represented societies. Motivated by the lack of annotated datasets for quantifying bias in under-represented societies, we endeavoured to create benchmark datasets for the New Zealand (NZ) population. We faced many challenges in this process, despite the availability of three annotators. This research outlines the manual annotation process, provides an overview of the challenges we encountered and lessons learnt, and presents recommendations for future research.