Evaluating the coherence of narrative sequences extracted from large document collections is crucial for applications in information retrieval and knowledge discovery. While mathematical coherence metrics based on embedding similarities provide objec...
Background: Electronic health records (EHRs) remain a vital source of clinical information, yet processing these heterogeneous data is extremely labor-intensive. Summarization of these data using Large Language Models (LLMs) is considered a promising...
Background: Large language models (LLMs) are increasingly integrated into clinical documentation, decision support, and patient-facing applications across healthcare, including plastic and reconstructive surgery. Yet, their evaluation remains bottlen...
Large language models (LLMs) are increasingly being utilized to develop tools and services in various domains, including education. However, due to the nature of the training data, these models are susceptible to inherent social or cognitive biases,...
The rapid growth of the body of literature on heavy metal hyperaccumulation in plants has created a critical bottleneck in data synthesis. Manual curation is slow, labor-intensive, and not scalable. To address this issue, we developed an artificial i...
The artificial generation of text by computers has been a field of study in computer science since the beginning of the twentieth century, from Markov chains to Turing tests. This has evolved into automatic summarization and marketing chatbots. The g...
As Large Language Models (LLMs) are core components in Retrieval-Augmented Generation (RAG) systems for knowledge-intensive tasks, concerns regarding hallucinations, redundancy, and unverifiable outputs have intensified, particularly in high-stakes d...
This paper presents the design, implementation and evaluation of an agentic virtual assistant (VA) for a medical clinic, combining large language models (LLMs) with retrieval-augmented generation (RAG) technology and multi-agent artificial intelligen...
We present a novel hybrid approach to literature-based discovery (LBD) which exploits large language models (LLMs) to enhance traditional LBD methodologies. We explore the use of LLMs to address significant LBD challenges: (1) the extraction of factu...
The recent advancements in large language models (LLMs) have brought significant changes to the field of education, particularly in the generation and evaluation of feedback. LLMs are transforming education by streamlining tasks like content creation...
This paper explores the novel application of large language models (LLMs) as evaluators for structured scientific summaries—a task where traditional natural language evaluation metrics may not readily apply. Leveraging the Open Research Knowled...
The rapid progress of Large Language Models (LLMs) has intensified the demand for flexible evaluation frameworks capable of accommodating diverse user needs across a growing variety of applications. While numerous standardized benchmarks exist for ev...
Enhancing fuel efficiency in public transportation requires the integration of complex multimodal data into interpretable, decision-relevant insights. However, traditional analytics and visualization methods often yield fragmented outputs that demand...
The pervasive deployment of large language models (LLMs) has given rise to mounting concerns regarding the safety and security of the content generated by these models. Nevertheless, the absence of comprehensive evaluation methods constitutes a subst...
Large language models (LLMs) are increasingly used as rubric-guided graders for short-answer exams, but their decisions can be unstable across prompts and vulnerable to answer-side prompt injection. In this paper, we study SteadyEval, a guardrailed e...
Common Weakness Enumerations (CWEs) and Common Vulnerabilities and Exposures (CVEs) are open knowledge bases that provide definitions, descriptions, and samples of code vulnerabilities. The combination of Large Language Models (LLMs) with vulnerabili...
A learning management system (LMS) plays a crucial role in supporting students’ educational activities by centralized platforms for course delivery, communication, and student support. Recently, many universities have integrated chatbots into t...
Integrating emotional intelligence into AI systems is essential for developing empathetic chatbots, yet deploying fully empathetic models is often constrained by business, ethical, and computational factors. We propose an innovative solution: a dedic...
Background: Large Language Models (LLMs) have demonstrated strong performances in clinical question-answering (QA) benchmarks, yet their effectiveness in addressing real-world consumer medical queries remains underexplored. This study evaluates the c...
The evaluation of large language models (LLMs) increasingly relies on other LLMs acting as automated judges. While this approach offers scalability and efficiency, it raises serious concerns regarding evaluator reliability, positional bias, and ranki...
The justice system has increasingly applied AI techniques for legal judgment to enhance efficiency. However, most AI techniques focus on decision-making outcomes, failing to capture the deliberative nature of the real-world judicial process. To addre...
Background and objectives: Large language models (LLMs) show promise in automating open-ended evaluation tasks, yet their reliability in rubric-based assessment remains uncertain. Variability in scoring, feedback, and rubric adherence raises concerns...
Root cause analysis (RCA) identifies the faults and vulnerabilities underlying software failures, informing better design and maintenance decisions. Earlier approaches typically framed RCA as a classification task, predicting coarse categories of roo...
Large language models (LLMs) judge three pairs of architectural design proposals which have been independently surveyed by opinion polls: department store buildings, sports stadia, and viaducts. A tailored prompt instructs the LLM to use specific emo...
Helicobacter pylori infects about half of the global population and is a major cause of peptic ulcer disease and gastric cancer. Improving patient education can increase screening participation, enhance treatment adherence, and help reduce gastric ca...
As recent advancements in large language models enhance reasoning across various domains, educators are increasingly exploring their use in conversation-based tutoring systems. However, since LLMs are black-box models to users and lack human-like pro...
Public Opinion Reports are essential tools for crisis management, yet their evaluation remains a critical bottleneck that often delays response actions. Recently, dominant Large Language Model (LLM)-based evaluators often overlook a critical challeng...
Background: Retrieval-augmented generation (RAG) aims to reduce hallucinations and outdated knowledge by grounding LLM outputs in retrieved evidence, but empirical results are scattered across tasks, systems, and metrics, limiting cumulative insight....
Rapid urbanization and the proliferation of heterogeneous urban data have intensified the challenges of semantic interoperability and integrated urban governance. To address this, we propose the Smart City Ontology Framework (SMOF), a standards-drive...
Background/Objectives: Large Language Models (LLMs), including ChatGPT-4 and Gemini, are increasingly incorporated into clinical care; however, their reliability within maternal–fetal medicine (MFM), a high-risk field in which diagnostic and ma...