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Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol

Created by
  • Haebom

Author

Wei Ma, Yixiao Yang, Qiang Hu, Shi Ying, Zhi Jin, Bo Du, Zhenchang Xing, Tianlin Li, Junjie Shi, Yang Liu, Linxiao Jiang

Outline

This paper addresses the challenges of quality assurance for large-scale language model (LLM) applications. We decompose LLM applications into three layers: the system shell layer, the prompt orchestration layer, and the LLM inference core layer, and evaluate the applicability of existing software testing methods to each layer. By analyzing the differences between testing methodologies in software engineering and AI, we identify six key challenges and propose four collaborative strategies (maintenance, transformation, integration, and runtime) to address them. Furthermore, we propose a closed-loop, reliable quality assurance framework that combines pre-deployment verification and runtime monitoring, as well as practical guidelines and protocols (AICL: Agent Interaction Communication Language) to support standardization and tooling for LLM application testing.

Takeaways, Limitations

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Takeaways:
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We provide a systematic approach to LLM application testing by presenting a hierarchical structure of LLM applications and proposing appropriate testing methodologies for each layer.
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We analyze the differences between testing methodologies in software engineering and AI and propose collaborative strategies to address them.
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We propose a closed-loop quality assurance framework and AICL protocol to lay the foundation for standardization and tooling of LLM application testing.
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Limitations:
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There is a lack of verification of the practical implementation and efficiency of the proposed AICL protocol.
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Further research is needed to determine generalizability across different types of LLM applications.
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Further research is needed to determine the practical applicability and effectiveness of the proposed collaboration strategy.
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