Large Language Models (LLM) in Industry: A Survey of Applications, Challenges, and Trends
Innovation such as domain-specific LLMs, LLM-as-a-Service (LLMaaS), and advancements in explainable AI (XAI) to enhance transparency and accessibility are examined to address challenges of large language models adoption.
Abstract
Large Language Models (LLMs) are transforming industries by automating tasks such as text generation, data analysis, and customer interactions. Their impact spans various sectors, including finance, healthcare, legal services, and education’ where they streamline operations and enhance decision-making. Despite these advantages, the adoption of LLMs is hindered by challenges such as high computational costs, data privacy concerns, and the lack of explainability. Existing surveys on LLMs primarily focus on their capabilities and applications, emphasizing their role in generating human-like text, processing unstructured data, and supporting decision-making. However, these studies also highlight the significant limitations of LLMs, particularly around computational expense, privacy, and the “black box” nature of their outputs, which restrict their use in critical, regulated industries. This paper builds on prior work by exploring emerging solutions to address these challenges. It examines innovations such as domain-specific LLMs, LLM-as-a-Service (LLMaaS), and advancements in explainable AI (XAI) to enhance transparency and accessibility. The paper provides practical insights into how businesses can strategically adopt LLMs while mitigating risks, making them more viable for broader industry application.