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.

While Large language models (LLMs) have shown to exhibit remarkable performance in a wide range of NLP tasks, they often struggle to interpret and reason over multi-hop questions in open-domain question answering (ODQA) settings. While popular prompt approaches such as Chain-of-Thought and Plan-and-Solve facilitate more manageable questions for OQDA via task decomposition, these approaches are prone to generating erroneous and redundant intermediate steps in multi-hop queries due to limited capacity for modeling complex entity relationships. In this paper, we introduce a novel prompt approach for multi-hop QA viz., MoDeGraph (Multi-Hop Dependency Graphs), that is designed to steer LLMs to extract and model entity relationships in complex questions. MoDeGraph constructs a dependency graph from LLM-generated entity-relation triples to enable more coherent and human-like multi-step reasoning. Experimental results in knowledge-intensive tasks for multi-hop QA demonstrate our approach produces more coherent and faithful reasoning chains as well as consistent increase in QA performance across several benchmark datasets.

Abstract Lexical Simplification (LS) is the task of substituting complex words within a sentence for simpler alternatives while maintaining the sentence’s original meaning. LS is the lexical component of Text Simplification (TS) systems with the aim of improving accessibility to various target populations such as individuals with low literacy or reading disabilities. Prior surveys have been published several years before the introduction of transformers, transformer-based large language models (LLMs), and prompt learning that have drastically changed the field of NLP. The high performance of these models has sparked renewed interest in LS. To reflect these recent advances, we present a comprehensive survey of papers published since 2017 on LS and its sub-tasks focusing on deep learning. Finally, we describe available benchmark datasets for the future development of LS systems.

Financial sentiment analysis can help in understanding market trends and help organizations and individuals make important business decisions. Several machine learning approaches have been used for financial sentiment analysis over the years ranging from lexicon-based approaches to the use of deep neural networks and transformer-based models. Recent advances in Large Language Models (LLM) have prompted the use of these models for various Natural Language Processing (NLP) tasks, however, these models have not yet been substantially explored in the financial domain. In this paper, we evaluate the performance of various LLMs and we introduce a small benchmark dataset consisting of excerpts extracted from the Federal Reserve chair’s speeches. We use this dataset along with other existing datasets to evaluate LLMs using in-context learning approaches. We compare the F1 scores of these models with the state-of-the-art BERT-based models and analyze our results.

Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks and have recently expanded their impact to coding tasks, bridging the gap between natural languages (NL) and programming languages (PL). This taxonomy-based survey provides a comprehensive analysis of LLMs in the NL-PL domain, investigating how these models are utilized in coding tasks and examining their methodologies, architectures, and training processes. We propose a taxonomy-based framework that categorizes relevant concepts, providing a unified classification system to facilitate a deeper understanding of this rapidly evolving field. This survey offers insights into the current state and future directions of LLMs in coding tasks, including their applications and limitations.

Task-oriented dialog systems have shown to foster effective human-chatbot collaborations for accomplishing goal-specific tasks through intent classification. In a real-world setting, collecting and training over user intents incurs a labeling-cost challenge for human annotators. While existing human-AI collaborative approaches such as active learning (AL) can properly resolve such labeling-cost challenges, most existing AL algorithms assume the unlabeled pool has similar distributions as the in domain (IND) training set. To address the conflict between AL and out-of-domain (OOD) data samples, we present Deep Contrastive Active Learning (DeCAL), a deep novel AL framework that uses contrastive learning techniques for query intent classification in task-oriented dialogs. DeCAL features an acquisition function that filters OOD samples by computing a distance-based confidence score over unlabeled samples using their neighboring features. To validate DeCAL, we compare against deep AL baselines via the performance of acquired IND/OOD samples and using the classification accuracy metric. Experimental results on benchmark datasets demonstrate De-CAL outperforms deep AL baseline algorithms on acquired OOD by 14%, while simultaneously showing competitive performance on IND accuracy.

Health communities online are popular for individuals to discuss health challenges and exchange social support. With social media, online communities also benefit neurodivergent individuals, by creating inclusive spaces where sharing of experience and knowledge is encouraged. The discussion in online communities covers a wide range of topics. As a result, the discussions differ in terms of topics, tone, and approach. This paper presents an analysis of social media posts shared on Reddit communities on Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD) between 2018 and 2020. In the study, we use a computer-aided model to identify prevalent topics in each subreddit and common themes. We conduct a comparative analysis of the communities and assess theme frequency and sentiment. The study highlights common topics found in r/adhd and r/autism subreddits, including diagnosis, treatment (medication dose and side effects), and social aspects (school, work, and peer interactions).