This paper provides a review of recent publications and working papers on ChatGPT and related Large Language Models (LLMs) in accounting and finance. The aim is to understand the current state of research in these two areas and identify potential research opportunities for future inquiry. We identify three common themes from these earlier studies. The first theme focuses on applications of ChatGPT and LLMs in various fields of accounting and finance. The second theme utilizes ChatGPT and LLMs as a new research tool by leveraging their capabilities such as classification, summarization, and text generation. The third theme investigates implications of LLM adoption for accounting and finance professionals, as well as for various organizations and sectors. While these earlier studies provide valuable insights, they leave many important questions unanswered or partially addressed. We propose venues for further exploration and provide technical guidance for researchers seeking to employ ChatGPT and related LLMs as a tool for their research.

This week, OpenAI launched what its chief executive, Sam Altman, called "the smartest model in the world"--a generative-AI program whose capabilities are supposedly far greater, and more closely approximate how humans think, than those of any such software preceding it. The start-up has been building toward this moment since September 12, a day that, in OpenAI's telling, set the world on a new path toward superintelligence. That was when the company previewed early versions of a series of AI models, known as o1, constructed with novel methods that the start-up believes will propel its programs to unseen heights. Mark Chen, then OpenAI's vice president of research, told me a few days later that o1 is fundamentally different from the standard ChatGPT because it can "reason," a hallmark of human intelligence. Shortly thereafter, Altman pronounced "the dawn of the Intelligence Age," in which AI helps humankind fix the climate and colonize space. As of yesterday afternoon, the start-up has released the first complete version of o1, with fully fledged reasoning powers, to the public.

Google, Meta, and Microsoft, as well as startups like OpenAI and Anthropic, all had well-developed strategies for generative AI by the time Apple finally announced its own push this June. Conventional wisdom suggested this entrance was unfashionably late. Its leaders say the company is arriving just in time--and that it's been stealthily preparing for this moment for years. That's part of the message I got from speaking with key Apple executives this fall about how they created what is now called Apple Intelligence. Senior vice president for software engineering Craig Federighi is a familiar character in an ongoing web series in the tech world known as keynote product launches.

OpenAI released its most advanced AI model yet, called o1, for paying users on Thursday. The launch kicked off the company's "12 Days of OpenAI" event--a dozen consecutive releases to celebrate the holiday season. OpenAI has touted o1's "complex reasoning" capabilities, and announced on Thursday that unlimited access to the model would cost 200 per month. In the video the company released to show the model's strengths, a user uploads a picture of a wooden birdhouse and asks the model for advice on how to build a similar one. The model "thinks" for a short period and then spits out what on the surface appears to be a comprehensive set of instructions. Close examination reveals the instructions to be almost useless.

With multiple AI platforms and bots competing against each other--there's Copilot, Gemini, ChatGPT, Claude, and Perplexity, to name just a few--we're seeing new updates and upgrades appear on a frequent basis. One of the newest additions OpenAI has pushed out to ChatGPT is called Canvas, and it's a little bit like an AI-powered Google Docs. OpenAI describes it as "a new way of working with ChatGPT to write and code," and it means you're essentially collaborating with the AI on a text document or on program code. You can already do this in the main chat interface of course, but with Canvas it's a bit more like having an AI coworker with you. Right now, you have to be a ChatGPT Enterprise, ChatGPT Pro, or ChatGPT Plus user (from 20 a month) to access the Canvas model.

Design platform Canva launched in 2013 with the aim of democratizing visual creation through features like templates and drag-and-drop graphics. It focused on ease, offering a design suite less daunting for nonprofessionals than tools like Adobe's Photoshop, and simplified access with a web platform and freemium model. Since then, the Sydney-headquartered company has grown to 170 million monthly active users and an 11-figure valuation. But with the advent of generative AI, it's having to innovate to keep its place. Cofounder and CEO Melanie Perkins insists she never saw AI as an existential threat and is excited to embrace it: This year, Canva acquired text-to-image generator Leonardo.ai

Anomaly detection (AD) is a critical machine learning task with diverse applications in web systems, including fraud detection, content moderation, and user behavior analysis. Despite its significance, AD in natural language processing (NLP) remains underexplored, limiting advancements in detecting anomalies in text data such as harmful content, phishing attempts, or spam reviews. In this paper, we introduce NLP-ADBench, the most comprehensive benchmark for NLP anomaly detection (NLP-AD), comprising eight curated datasets and evaluations of nineteen state-of-the-art algorithms. These include three end-to-end methods and sixteen two-step algorithms that apply traditional anomaly detection techniques to language embeddings generated by bert-base-uncased and OpenAI's text-embedding-3-large models. Our results reveal critical insights and future directions for NLP-AD. Notably, no single model excels across all datasets, highlighting the need for automated model selection. Moreover, two-step methods leveraging transformer-based embeddings consistently outperform specialized end-to-end approaches, with OpenAI embeddings demonstrating superior performance over BERT embeddings. By releasing NLP-ADBench at https://github.com/USC-FORTIS/NLP-ADBench, we provide a standardized framework for evaluating NLP-AD methods, fostering the development of innovative approaches. This work fills a crucial gap in the field and establishes a foundation for advancing NLP anomaly detection, particularly in the context of improving the safety and reliability of web-based systems.

Clustering based on vibration responses, such as transmissibility functions (TFs), is promising in structural anomaly detection, but most existing approaches struggle with determining the optimal cluster number and handling high-dimensional streaming data, while their shallow structures also make them sensitive to manually-engineered feature quality. To bridge this gap, this work proposes the Dirichlet process-deep generative model-integrated incremental learning (DPGIIL) for clustering by combining the advantages of deep generative models (DGMs) in representation learning and the Dirichlet process mixture model (DPMM) in identifying distinct patterns in observed data. By introducing a DPMM prior into the latent space of DGMs, DPGIIL automatically captures dissimilarities in extracted latent representations, enabling both generative modeling and clustering. Within the context of variational Bayesian inference, a lower bound on the log marginal likelihood of DPGIIL, tighter than the evidence lower bound given sufficient training data, is derived analytically, which enables the joint optimization of DGM and DPMM parameters, thereby allowing the DPMM to regularize the DGM's feature extraction process. Additionally, a greedy split-merge scheme-based coordinate ascent variational inference method is devised to accelerate the optimization. The summary statistics of the DPMM, along with the network parameters, are used to retain information about previous data for incremental learning. Notably, this study uses variational autoencoder (VAE) within DPGIIL as an illustrative example, while this framework is adaptable to other DGMs. Two case studies show that the proposed method outperforms some state-of-the-art approaches in structural anomaly detection and clustering, while also dynamically generating new clusters to indicate the emergence of new structural conditions for online monitoring.

This paper introduces a framework that leverages Large Language Models (LLMs) to answer natural language queries about General Transit Feed Specification (GTFS) data. The framework is implemented in a chatbot called TransitGPT with open-source code. TransitGPT works by guiding LLMs to generate Python code that extracts and manipulates GTFS data relevant to a query, which is then executed on a server where the GTFS feed is stored. It can accomplish a wide range of tasks, including data retrieval, calculations, and interactive visualizations, without requiring users to have extensive knowledge of GTFS or programming. The LLMs that produce the code are guided entirely by prompts, without fine-tuning or access to the actual GTFS feeds. We evaluate TransitGPT using GPT-4o and Claude-3.5-Sonnet LLMs on a benchmark dataset of 100 tasks, to demonstrate its effectiveness and versatility. The results show that TransitGPT can significantly enhance the accessibility and usability of transit data.

This paper investigates the suitability of frontier Large Language Models (LLMs) for Q&A interactions in science centres, with the aim of boosting visitor engagement while maintaining factual accuracy. Using a dataset of questions collected from the National Space Centre in Leicester (UK), we evaluated responses generated by three leading models: OpenAI's GPT-4, Claude 3.5 Sonnet, and Google Gemini 1.5. Each model was prompted for both standard and creative responses tailored to an 8-year-old audience, and these responses were assessed by space science experts based on accuracy, engagement, clarity, novelty, and deviation from expected answers. The results revealed a trade-off between creativity and accuracy, with Claude outperforming GPT and Gemini in both maintaining clarity and engaging young audiences, even when asked to generate more creative responses. Nonetheless, experts observed that higher novelty was generally associated with reduced factual reliability across all models. This study highlights the potential of LLMs in educational settings, emphasizing the need for careful prompt engineering to balance engagement with scientific rigor.