How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks

Performance can drop dramatically under real-world conditions: GPT-3.5's accuracy fell by up to 43.59% on sentiment analysis tasks and 35.74% on natural language inference when tested with text variations that simulate real-world complexity, suggesting the model's strong benchmark performance may not translate to reliable performance in practice.

Current AI systems are sensitive to minor input changes: The study identified "prompt sensitivity" and "number sensitivity" as specific vulnerabilities, meaning GPT-3.5 can produce inconsistent results based on slight rewording or numerical variations in inputs—a concerning finding for applications requiring stable, trustworthy performance.

Robustness testing is essential for responsible AI deployment: By testing GPT-3.5 across 21 datasets with 116,000 test samples and 66 different text transformations, the researchers demonstrated that comprehensive robustness evaluation—not just accuracy on standard benchmarks—is necessary to assess whether AI systems are truly ready for real-world use.

"Robustness instability" reveals unpredictable behavior: The paper identifies inconsistent robustness across different types of text variations, meaning GPT-3.5 fails predictably on some challenges but unpredictably on others, making it difficult for users to anticipate when the system might malfunction.

Better performance on benchmarks doesn't guarantee trustworthiness: Despite GPT-3.5 outperforming some fine-tuned models on standard tasks, its significant robustness degradation highlights that benchmark rankings alone are insufficient measures of AI reliability and that stability under diverse conditions must be evaluated separately.