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The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

This study aims to investigate the effect of galvanic corrosion on the sulfide stress corrosion cracking (SSCC) of X80/Inconel 625 weld overlay by altering the cathode/anode area ratios, Na₂S₂O₃ concentrations and temperatures.

The effects of galvanic corrosion on X80/Inconel 625 weld overlay SSCC were investigated by immersion test, galvanic corrosion current test, electrochemical measurement, four-point bending experiment, hydrogen permeation experiment and scanning electron microscopy.

The anodic dissolution of the fusion boundary was enhanced as the cathode/anode area ratio increased, which is necessary for the SSCC of the X80/Inconel 625 weld overlay. However, severe galvanic corrosion reduced the SSCC susceptibility. The SSCC susceptibility showed a linear increase with Na₂S₂O₃ concentration in the range of 10⁻⁴ ∼ 10⁻² mol/L. However, further increasing the Na₂S₂O₃ concentration to 10⁻¹ mol/L resulted in the disappearance of SSCC. This is likely because sufficient hydrogen was required for SSCC initiation even under severe anodic dissolution conditions, which was further supported by the reduced SSCC susceptivity at elevated temperatures.

Limited studies aim to establish the relationship between the galvanic corrosion and SSCC of welded joints through altering the cathode/anode area ratios, Na₂S₂O₃ concentrations and temperatures. This work will pave the way for understanding the effect of galvanic corrosion on the SSCC of dissimilar weld joints.