Related papers: Evaluating Structured Documentation as a Tool for …
Technology development practices in industry are often primarily focused on business results, which risks creating unbalanced power relations between corporate interests and the needs or concerns of people who are affected by technology…
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems…
Retrieving relevant documents from a corpus is typically based on the semantic similarity between the document content and query text. The inclusion of structural relationship between documents can benefit the retrieval mechanism by…
Artificial Intelligence (AI) has made its way into various scientific fields, providing astonishing improvements over existing algorithms for a wide variety of tasks. In recent years, there have been severe concerns over the trustworthiness…
Software documentation guides the proper use of tools or services. With the rapid growth of machine learning libraries, individuals from various fields are incorporating machine learning into their workflows through programming. However,…
Although numerous ethics courses are available, with many focusing specifically on technology and computer ethics, pedagogical approaches employed in these courses rely exclusively on texts rather than on software development or data…
The effective utilization of structured data, integral to corporate data strategies, has been challenged by the rise of large language models (LLMs) capable of processing unstructured information. This shift prompts the question: can LLMs…
Large language models (LLMs) have achieved remarkable success across various domains, driving significant technological advancements and innovations. Despite the rapid growth in model scale and capability, systematic, data-driven research…
Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and…
Understanding learning as a dynamic process is challenging due to the interaction of multiple factors, including cognitive load, internal state change, and subjective evaluation. Existing approaches often address these elements in…
Retrieval systems often fail when user queries differ stylistically or semantically from the language used in domain documents. Query rewriting has been proposed to bridge this gap, improving retrieval by reformulating user queries into…
Good communication is vital in healthcare, both among healthcare professionals, and between healthcare professionals and their patients. And well-written documents, describing and/or explaining the information in structured databases may be…
This study examines the development of reflective practice among students on a four-year work-based Software Engineering program. Using two established models of reflection - Boud et al.'s Model of Reflective Process and Bain et al.'s 5R…
Context: Behaviour Driven Development (BDD) uses scenarios written in semi-structured natural language to express software requirements in a way that can be understood by all stakeholders. The resulting natural language specifications can…
Fairness, Accountability, and Transparency (FAccT) for socio-technical systems has been a thriving area of research in recent years. An ACM conference bearing the same name has been the central venue for scholars in this area to come…
This paper embarks on an exploration into the Large Language Model (LLM) datasets, which play a crucial role in the remarkable advancements of LLMs. The datasets serve as the foundational infrastructure analogous to a root system that…
Relational databases (RDBs) are widely regarded as the gold standard for storing structured information. Consequently, predictive tasks leveraging this data format hold significant application promise. Recently, Relational Deep Learning…
Structured reasoning can improve the inference performance of large language models (LLMs), but it also introduces computational cost and control constraints. When additional reasoning structure helps, and when it instead reduces efficiency…
Large language models (LLMs) have shown impressive performance on general-purpose tasks, yet adapting them to specific domains remains challenging due to the scarcity of high-quality domain data. Existing data synthesis tools often struggle…
Computational research and data analytics increasingly relies on complex ecosystems of open source software (OSS) "libraries" -- curated collections of reusable code that programmers import to perform a specific task. Software documentation…