BIG-bench & MMLU (2023) › Limitations

Despite their significant contributions, BIG-bench and MMLU faced limitations that became apparent as the field continued to evolve. One fundamental concern was whether performance on these benchmarks truly reflected general capabilities or merely optimization for specific task types. Models achieving high MMLU scores might have learned to recognize patterns in academic examinations without understanding the underlying concepts. Similarly, strong BIG-bench performance might reflect sophisticated pattern matching rather than reasoning.

The evaluation methodology in BIG-bench, while flexible, created challenges for comparison. Different tasks used different metrics, making it difficult to aggregate performance or compare models consistently. Some tasks required human evaluation, which introduced subjectivity and inconsistency. The benchmark's emphasis on diversity sometimes came at the cost of standardization, making evaluation across the full suite complex.

MMLU's multiple-choice format, while enabling standardized evaluation, had limitations. Real-world applications often require generating explanations, showing work, or providing answers with qualifications and context that multiple-choice questions cannot capture. A model might select the correct answer without understanding the reasoning behind it, or might understand the concept but struggle with the specific phrasing of answer choices.

The benchmarks' focus on English language tasks limited their applicability to multilingual systems. Both BIG-bench and MMLU primarily evaluated English capabilities. This provided limited assessment of cross-lingual understanding or performance in other languages. As language models were deployed globally, this limitation became increasingly problematic.

Cultural bias presented another challenge. MMLU's use of academic examinations from Western educational systems might not reflect knowledge structures or evaluation approaches in other cultures. BIG-bench tasks, while diverse, were created primarily by researchers from Western institutions, potentially introducing cultural assumptions that affected evaluation fairness.

The benchmarks' difficulty calibration also raised concerns. As models improved, tasks that had once been challenging became routine, leading to saturation similar to what had occurred with earlier benchmarks. Maintaining appropriate difficulty levels required ongoing updates and new task selection, creating challenges for benchmark longevity and consistency over time.

The cost and complexity of running full evaluations on these benchmarks limited their accessibility. Evaluating all 200+ BIG-bench tasks required substantial computational resources. MMLU's 57 tasks, while more manageable, still required significant evaluation effort. Running either full suite was difficult for researchers with limited resources.

Interpretability challenges also emerged. BIG-bench's diverse tasks, while valuable for testing breadth, sometimes made it difficult to understand why models succeeded or failed on specific tasks. The benchmark's emphasis on diversity sometimes came at the cost of diagnostic clarity. MMLU, while more standardized, still left questions about whether performance reflected knowledge depth or test-taking strategies.