Yuhao Ye


2024

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Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey
Jiawei Li | Yizhe Yang | Yu Bai | Xiaofeng Zhou | Yinghao Li | Huashan Sun | Yuhang Liu | Xingpeng Si | Yuhao Ye | Yixiao Wu | 林一冠 林一冠 | Bin Xu | Ren Bowen | Chong Feng | Yang Gao | Heyan Huang
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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.