This survey provides an in-depth review of large language models (LLMs), highlighting the significant paradigm shift they represent in artificial intelligence. Our purpose is to consolidate state-of-the-art advances in LLM design, training, adaptation, evaluation, and application for both researchers and practitioners. To accomplish this, we trace the evolution of language models and describe core approaches, including parameter-efficient fine-tuning (PEFT). The methodology involves a thorough survey of real-world LLM applications across the scientific, engineering, healthcare, and creative sectors, coupled with a review of current benchmarks. Our findings indicate that high training and inference costs are shaping market structures, raising economic and labor concerns, while also underscoring a persistent need for human oversight in assessment. Key trends include the development of unified multimodal architectures capable of processing varied data inputs and the emergence of agentic systems that exhibit complex behaviors such as tool use and planning. We identify critical open problems, such as detectability, data contamination, generalization, and benchmark diversity. Ultimately, we conclude that overcoming these complex technical, economic, and social challenges necessitates collaborative advancements in adaptation, evaluation, infrastructure, and governance.

In this article we present new evidence of cross-border shopping in response to sales taxation. While several instructive studies provide estimates of the cross-border shopping effect, we utilize a unique opportunity to evaluate the effect of a large discrete change in sales tax policy. Using county level data on food sales and sales tax rates for West Virginia over the 1988-1991 period we estimate that for every one-percentage point increase in the county relative price ratio due to the sales tax change, per capita food sales decreased by about 1.38 percent. Our estimates indicate that food sales fell in West Virginia border counties by about eight percent as a result of the imposition of the six percent sales tax on food at the beginning of 1990.

Local governments play an important role in the COVID-19 pandemic response. They can identify the local vulnerabilities and respond accordingly. In this commentary, we are examining the relationship between COVID-19 case and fatality numbers and provincial governments in Indonesia using correlations and data visualization. We use the public health development index, regional sustainable development index, competitiveness index, and their components as proxies for provincial government capacity and strength. We have found some evidence of a negative relationship between COVID-19 case fatality and death rates and various indices of provincial strength. The correlations are higher when we examine provinces in island regions, such as Java and Sumatra, separately. Some of our findings suggest that stronger provincial governments are a good defense against the pandemic. At the same time there are differences between regions, which is a concern in terms of all-of-government response to the pandemic.

This survey offers an in-depth review of Large Language Models (LLMs), highlighting the significant paradigm shift they represent in artificial intelligence. Our purpose is to consolidate state-of-the-art advances in LLM design, training, adaptation, evaluation, and application for both researchers and practitioners. To accomplish this, we trace the evolution of language models and describe core approaches, including Parameter-Efficient Fine-Tuning (PEFT). The methodology involves a thorough survey of real-world LLM applications across the scientific, engineering, healthcare, and creative sectors, coupled with a review of current benchmarks. Our findings indicate that high training and inference costs are shaping market structures, raising economic and labour concerns, while also underscoring a persistent need for human oversight in assessment. Key trends include the development of multimodal and agentic systems. We identify critical open problems, such as detectability, data contamination, generalisation, and benchmark diversity. Ultimately, we conclude that overcoming these complex technical, economic, and social challenges necessitates collaborative advancements in adaptation, evaluation, infrastructure, and governance.