LLMORPH: Automated Metamorphic Testing of Large Language Models
This addresses the problem of evaluating LLM reliability for researchers and developers, though it is incremental as it applies an existing testing paradigm to LLMs.
The authors tackled the challenge of automated testing for Large Language Models (LLMs) by developing LLMORPH, a tool that uses Metamorphic Testing to detect inconsistencies in model outputs without human-labeled data, resulting in over 561,000 test executions across three state-of-the-art LLMs.
Automated testing is essential for evaluating and improving the reliability of Large Language Models (LLMs), yet the lack of automated oracles for verifying output correctness remains a key challenge. We present LLMORPH, an automated testing tool specifically designed for LLMs performing NLP tasks, which leverages Metamorphic Testing (MT) to uncover faulty behaviors without relying on human-labeled data. MT uses Metamorphic Relations (MRs) to generate follow-up inputs from source test input, enabling detection of inconsistencies in model outputs without the need of expensive labelled data. LLMORPH is aimed at researchers and developers who want to evaluate the robustness of LLM-based NLP systems. In this paper, we detail the design, implementation, and practical usage of LLMORPH, demonstrating how it can be easily extended to any LLM, NLP task, and set of MRs. In our evaluation, we applied 36 MRs across four NLP benchmarks, testing three state-of-the-art LLMs: GPT-4, LLAMA3, and HERMES 2. This produced over 561,000 test executions. Results demonstrate LLMORPH's effectiveness in automatically exposing inconsistencies.