BIG-Bench Extra Hard (2502.19187v2)

Abstract: LLMs are increasingly deployed in everyday applications, demanding robust general reasoning capabilities and diverse reasoning skillset. However, current LLM reasoning benchmarks predominantly focus on mathematical and coding abilities, leaving a gap in evaluating broader reasoning proficiencies. One particular exception is the BIG-Bench dataset, which has served as a crucial benchmark for evaluating the general reasoning capabilities of LLMs, thanks to its diverse set of challenging tasks that allowed for a comprehensive assessment of general reasoning across various skills within a unified framework. However, recent advances in LLMs have led to saturation on BIG-Bench, and its harder version BIG-Bench Hard (BBH). State-of-the-art models achieve near-perfect scores on many tasks in BBH, thus diminishing its utility. To address this limitation, we introduce BIG-Bench Extra Hard (BBEH), a new benchmark designed to push the boundaries of LLM reasoning evaluation. BBEH replaces each task in BBH with a novel task that probes a similar reasoning capability but exhibits significantly increased difficulty. We evaluate various models on BBEH and observe a (harmonic) average accuracy of 9.8\% for the best general-purpose model and 44.8\% for the best reasoning-specialized model, indicating substantial room for improvement and highlighting the ongoing challenge of achieving robust general reasoning in LLMs. We release BBEH publicly at: https://github.com/google-deepmind/bbeh.

Critical Analysis of the BBEH Benchmark

The "BIG-Bench Extra Hard" (BBEH) paper addresses critical gaps in the evaluation of LLMs' (LLMs) reasoning abilities. The benchmark expands on the limitations identified in existing frameworks, particularly the saturation of previous benchmarks like BIG-Bench and BIG-Bench Hard (BBH), by proposing more challenging tasks. Recognizing the near ceiling performance achieved by state-of-the-art models on BBH tasks, the authors introduce BBEH to extend evaluation to a broader array of reasoning skills.

Key Contributions and Methodology

The paper highlights several crucial improvements over its predecessors:

1. Enhanced Task Difficulty: BBEH aims to push the current LLMs by replacing every task in BBH with a significantly harder variant. State-of-the-art models, such as reasoning-specialized and general-purpose LLMs, achieved harmonic mean accuracy of 9.8% and 44.8%, respectively, on BBEH, suggesting ample room for improvement and the benchmark's capacity to discriminate performance even among top-tier performers.
2. Broader Reasoning Scope: Unlike BIG-Bench and BBH, which predominantly tested mathematical and coding proficiencies or a narrow set of reasoning skills, BBEH emphasizes a diverse set of cognitive tasks. This includes many-hop reasoning, long-range dependency, learning on the fly, processing distractors, temporal understanding, and identifying errors in reasoning traces.
3. Task Design and Fair Evaluation: BBEH introduces tasks like "Buggy Tables," "Causal Understanding," and "Spatial Reasoning," each designed to evaluate skills beyond mere quantitative measures. For instance, it assesses LLMs' abilities to reconstruct buggy tables or deduce unknown variables within spatial puzzles, which traditional benchmarks rarely tackle.
4. Sophistication in Evaluation: By adopting a semi-adversarial approach where tasks are iteratively refined against strong reference models (Gemini 1.5 Flash and Gemini 2.0-Flash-Thinking-Exp-01-21), the paper ensures that the problems posed continue to stretch the capabilities of frontier models. This rigorous calibration results in a benchmark that remains challenging across LLM generations, avoiding the rapid obsolescence that hindered earlier benchmarks.

Implications for AI Research

The introduction of BBEH is likely to have profound implications in both theoretical and practical fronts:

• Model Robustness and Generalization: By scoring models on a harmonic mean that penalizes inconsistency across tasks, BBEH encourages the development of LLMs that are robustly capable of general reasoning. This is a significant shift from benchmarks that favored excellence in niche areas.
• Rich Diagnostic Insight: The detailed task-specific analyses within BBEH can offer deep diagnostic insights into modes of failure and areas requiring model improvement. Such insights are invaluable for designing future LLM architectures with enhanced cognitive faculties.
• Benchmark Sustainability: With its comprehensive task instructions, fine-grained evaluation metrics, and transparency in results, BBEH sets a standard not only for evaluating current LLMs but for ensuring that future models are grounded in a test set that redefines meaningful progression in the field.

Speculation on Future Developments

The BBEH sets the stage for a strategic pivot in LLM research focus. As BBEH challenges current models, researchers may need to innovate mechanisms that allow models to better integrate knowledge, learn dynamically, and adapt to complex reasoning tasks. This pivot will likely catalyze advances in model architecture, such as models that more effectively use recurrent memory to handle long-context understanding or those that employ multi-step, parallel reasoning paths to manage abstract, nuanced tasks beyond conventional scope.

In conclusion, the BBEH benchmark distinguishes itself through its robust task diversification and surmountable challenge level. It paves the way for foundational advances in language technology, set against the backdrop of rapidly evolving AI landscapes, while remaining accessible to future state-of-the-art developments.