BIG-bench & MMLU (2023) › The Problem

The rapid evolution of large language models between 2020 and 2023 exposed fundamental limitations in how the field evaluated language understanding capabilities. Traditional benchmarks like GLUE, SuperGLUE, and SQuAD, which had driven significant progress in earlier years, were becoming saturated. Models were achieving near-perfect scores on many tasks, making it difficult to distinguish between different systems or to identify areas where capabilities still needed improvement. The benchmarks had served their purpose in driving progress, but as models improved, the limitations of these evaluation frameworks became increasingly apparent.

One fundamental limitation was the narrow scope of traditional benchmarks. GLUE and SuperGLUE focused primarily on English language NLP tasks like sentiment analysis, textual entailment, and question answering. While these tasks tested important aspects of language understanding, they represented only a small fraction of the capabilities that large language models were demonstrating. Models could summarize long documents, write code, solve mathematical problems, answer questions about specialized domains, and engage in complex reasoning. Traditional benchmarks provided little or no evaluation of these broader capabilities.

The task types in existing benchmarks were also limited. Most tasks required classification or span extraction, which tested understanding but not generation, reasoning, or creative problem-solving. Models might excel at determining whether one sentence entailed another but struggle with tasks requiring multi-step reasoning, knowledge application across domains, or creative thinking. The benchmark suite lacked diversity in task formats, missing important capabilities like mathematical problem-solving, scientific reasoning, coding ability, or analysis of complex arguments.

Another critical limitation was the absence of specialized knowledge evaluation. Large language models were being applied to domains requiring expert knowledge, from legal research to medical diagnosis support. Traditional benchmarks provided no way to assess whether models possessed the specialized knowledge needed for these applications. A model might perform well on general language understanding tasks while lacking the domain-specific knowledge required for professional applications. This gap made it difficult to assess the practical utility of language models in specialized contexts.

The difficulty level of existing benchmarks also became problematic. As models improved, tasks that had once been challenging became routine. This saturation meant that benchmarks could no longer distinguish between different models or track incremental progress. When most models achieve similar high scores on a benchmark, that benchmark loses its diagnostic value. The field needed new benchmarks with appropriate difficulty levels that could challenge modern models while remaining interpretable.

Evaluation methodology presented additional challenges. Traditional benchmarks typically used single-metric evaluation focused on accuracy or F1 scores. While these metrics provided objective measures, they might not capture important aspects of performance like reasoning quality, answer plausibility, or the ability to handle edge cases. A model might achieve high accuracy on a benchmark while producing answers that, while technically correct, lack the nuance or depth that would be expected in real-world applications.

The cultural and linguistic bias in existing benchmarks was another significant limitation. Most benchmarks focused exclusively on English language tasks. This provided limited evaluation of multilingual capabilities or cross-cultural understanding. As language models were deployed globally, this limitation became increasingly problematic. The field needed benchmarks that could assess different languages and cultures beyond Western academic contexts.

For researchers developing large language models, the lack of evaluation frameworks spanning these capabilities created uncertainty. Without benchmarks that tested the full range of abilities these models demonstrated, it was difficult to assess strengths and weaknesses, guide development priorities, or compare approaches directly. The field needed evaluation suites that could keep pace with modern language models.