Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024)

Abstract: Large Language Models (LLMs) demonstrate significant value in domain-specific applications, benefiting from their fundamental capabilities. Nevertheless, it is still unclear which fundamental capabilities contribute to success in specific domains. Moreover, the existing benchmark-based evaluation cannot effectively reflect the performance of real-world applications. In this survey, we review recent advances of LLMs in domain applications, aiming to summarize the fundamental capabilities and their collaboration. Furthermore, we establish connections between fundamental capabilities and specific domains, evaluating the varying importance of different capabilities. Based on our findings, we propose a reliable strategy for domains to choose more robust backbone LLMs for real-world applications.

## introduction in the current research and application of artificial intelligence, the abundant acquisition of big data, breakthroughs in high-performance computing technology, and innovations in algorithm design have jointly promoted the development and deployment of llms @xcite . llms are also considered to have strong potential value in specific domains, with an increasing number of industries embracing llms and already demonstrating outstanding performance @xcite @xcite . however, applying llms in specific domains has encountered a series of challenges. these challenges mainly stem from the inherent characteristics of domain tasks and data, such as the diversity of data sources, the complexity of domainspecific knowledge, and the specificity of application goals and constraints. to enable llms to be better applied in specific domains and address the challenges they face in these areas, this paper * corresponding author. issue 1: fundamental capabilities of llms and their interactions. llms exhibit outstanding performance in comprehending and addressing complex tasks, thus demonstrating great potential in specific domain applications. numerous studies broadly summarize the core capabilities of llms as robust comprehension and generation @xcite ), yet fall short of assisting us in aligning the llms' fine-grained capabilities with the intricate requirements of realworld scenarios. consequently, elucidating the inherent fundamental capabilities manifested by llms in domain-specific scenarios and the dynamics among these capabilities becomes essential. issue 2: the capabilities assessment of llms in specific domain. due to the disparity between the capabilities evaluated in benchmarks and those required in real-world domains @xcite , the excellent performance of llms in benchmarks may not necessarily translate to actual applications in specific domains. therefore, conducting capabilities assessments of llms to establish a bridge between benchmarks and real-world domains is crucial. based on the above two issues, this survey aims to systematically summarize the fundamental capabilities of llms and clarify the capabilities assessment of llms. the key contributions of this survey paper are summarized below. ## fundamental capabilities and interactive capabilities the human brain's information processing has been extensively studied, revealing five core modules: natural language interaction, knowledge, memorization, reasoning, and generalization @xcite . some research suggests that llms exhibit a similar information processing mechanism to the human brain @xcite . consequently, we categorize llm information processing into four fundamental capabilities including memorization, reasoning, generalization, and diversification. as illustrated in figure 1 , llms utilize short-term memory to understand task instructions and longterm memory to retrieve historical data @xcite @xcite @xcite @xcite . this data is processed through the reasoning module, which performs logical, commonsense, and symbolic reasoning to generate outputs @xcite @xcite @xcite . throughout this process, the generalization enables llms to manage information across varying lengths, structures, and tasks @xcite @xcite , while diversification allows for tailored results @xcite . the four fundamental capabilities of the llm work together to complete complex domain applications. in the following section, we will introduce each of the fundamental capabilities in detail. ## the capabilities assessment of llms in specific domains llms have different applications in different domain scenarios. for instance, in the medical and ledge domains, they may function as domain experts engaged in dialogues or summarizing documents @xcite @xcite . systematically summarizing the application methods of llms in various domains facilitates to combine these models with specific scenarios more efficiently. however, some research is often classified and summarized from the perspective of nlp tasks @xcite . kaddour et al. (2023) classify applications in medical scenarios into medical question answering and comprehension, and medical information retrieval. however, llms may participate in medical diagnosis, diagnostic assistance, and other scenarios. the differences make it difficult for research results to be directly applied to real scenarios. we enumerates articles from nine domains we have summarized in figure 3 , including medicine, law, computational biology, finance, social sciences and psychology, computer programming and software engineering, robots and agents, ai for disciplines, and creative work. we will provide a detailed summary of the real-world applications and roles played by llms in these domains in the future. ## conclusion in this paper, we summarize the fundamental capabilities of llms in domain applications and illustrate how they collaborate. simultaneously, we summarize the applications of llms in various domains from real-world perspectives. furthermore, we outline the key capabilities emphasized in different domains, aiding users in more accurately applying llms in domain applications.