arXiv:2504.11972v3 Announce Type: replace Abstract: Extractive QA tasks are commonly evaluated using Exact Match (EM) and F1-score, but these metrics often fail to reflect true model performance. Recent studies have proposed using large language models (LLMs) as judges (LLM-as-a-judge), yet they often lack comprehensive evaluation across datasets and overlook key factors such as sensitivity to answer types, prompt variations, and self-preference bias. In this work, we conduct a systematic study of LLM-as-a-judge across four extractive QA datasets and various prompt variations, assessing multip
The increased reliance on LLMs for complex tasks necessitates more robust evaluation methods, and traditional metrics are proving insufficient for current AI capabilities.
This work directly addresses the fundamental challenge of reliably evaluating advanced AI systems, which is crucial for their safe and effective deployment and for understanding their true performance limits.
The understanding of LLM-as-a-judge capabilities and limitations is refined, suggesting more nuanced approaches are required for assessing AI performance beyond simple metrics.
- · AI developers focused on robust evaluation
- · Researchers improving AI safety and alignment
- · Benchmarking platforms adopting advanced metrics
- · Developers solely relying on EM/F1 for QA
- · Users overestimating LLM-as-a-judge reliability without deeper analysis
Improved and more sophisticated evaluation frameworks for AI models will emerge.
This will lead to a clearer understanding and potentially slower, more deliberate adoption of LLMs in critical applications due to better performance assessment.
Long-term, this could foster greater trust in AI systems as their evaluation becomes more transparent and reliable, accelerating their integration into new domains.
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