  Large-Language Models (LLMs) have shifted the paradigm of natural language
data processing. However, their black-boxed and probabilistic characteristics
can lead to potential risks in the quality of outputs in diverse LLM
applications. Recent studies have tested Quality Attributes (QAs), such as
robustness or fairness, of LLMs by generating adversarial input texts. However,
existing studies have limited their coverage of QAs and tasks in LLMs and are
difficult to extend. Additionally, these studies have only used one evaluation
metric, Attack Success Rate (ASR), to assess the effectiveness of their
approaches. We propose a MEtamorphic Testing for Analyzing LLMs (METAL)
framework to address these issues by applying Metamorphic Testing (MT)
techniques. This approach facilitates the systematic testing of LLM qualities
by defining Metamorphic Relations (MRs), which serve as modularized evaluation
metrics. The METAL framework can automatically generate hundreds of MRs from
templates that cover various QAs and tasks. In addition, we introduced novel
metrics that integrate the ASR method into the semantic qualities of text to
assess the effectiveness of MRs accurately. Through the experiments conducted
with three prominent LLMs, we have confirmed that the METAL framework
effectively evaluates essential QAs on primary LLM tasks and reveals the
quality risks in LLMs. Moreover, the newly proposed metrics can guide the
optimal MRs for testing each task and suggest the most effective method for
generating MRs.
