METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities¶
Arxiv Link - 2023-12-11 01:29:19
Abstract¶
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.
Socials¶
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| 🚀 Exciting news in the world of Large-Language Models (LLMs)! Recent studies have introduced the METAL framework, a groundbreaking approach that utilizes Metamorphic Testing techniques to systematically evaluate the quality attributes of LLMs. By defining Metamorphic Relations (MRs) as modularized evaluation metrics, METAL can generate hundreds of MRs covering various qualities and tasks in LLMs. The experiments conducted with three major LLMs have demonstrated that the METAL framework effectively identifies essential Quality Attributes, helping to uncover potential risks in LLM outputs. Additionally, novel metrics have been introduced to enhance the assessment accuracy by integrating semantic qualities of text. Curious to learn more about this innovative approach and its implications for the future of LLM testing? Check out the full study here: http://arxiv.org/abs/2312.06056v1 #LLM #MetamorphicTesting #QualityAttributes #TechInnovation #AI #NLP #ResearchStudy #TechNews |
🚀 Exciting new research alert! Discover how the METAL framework enhances the evaluation of Large-Language Models (LLMs) in diverse applications by utilizing Metamorphic Testing techniques. Find out more at: http://arxiv.org/abs/2312.06056v1 #AI #NLP #LLMs #METALframework #TechResearch |