{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench","slug":"beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench","name":"Beyond the Imitation Game: Quantifying and extrapolating the capabilities of lang... (BIG-bench)","type":"benchmark","url":"https://arxiv.org/abs/2206.04615","page_url":"https://unfragile.ai/beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench","categories":["productivity"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"inactive","verified":false},"capabilities":[{"id":"awesome-beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench__cap_0","uri":"capability://data.processing.analysis.standardized.task.based.capability.evaluation","name":"standardized-task-based-capability-evaluation","description":"Provides a curated suite of 204 diverse tasks spanning reasoning, language understanding, code generation, and knowledge domains that enable quantitative measurement of language model capabilities. Tasks are structured as input-output pairs with standardized evaluation metrics (accuracy, F1, BLEU, etc.), allowing researchers to run their own models against fixed benchmarks and generate comparable performance scores across different LLM architectures and sizes.","intents":["I need to objectively measure how well my language model performs across diverse capability domains","I want to compare my model's performance against other models using the same standardized evaluation criteria","I need to identify capability gaps in my model by seeing which task categories it underperforms on"],"best_for":["LLM researchers and model developers at AI labs evaluating new architectures","practitioners benchmarking commercial models (GPT-3, PaLM, Claude) against a standard","academic researchers studying how language model capabilities scale with model size"],"limitations":["204 tasks, while broad, cannot comprehensively cover all real-world use cases or domain-specific requirements","evaluation metrics are task-dependent and some use proxy metrics (BLEU for generation) rather than human judgment, potentially missing nuanced capability differences","no built-in handling of task contamination — benchmark tasks may overlap with model training data, inflating performance estimates","requires user to supply and run their own LLM inference; benchmark provides no inference service"],"requires":["a language model to evaluate (local or via API)","computational resources to run inference (GPU recommended for efficiency)","Python 3.7+ to load and execute benchmark tasks","understanding of evaluation metrics and statistical interpretation"],"input_types":["text prompts","structured problem definitions (math, logic, code)","multiple-choice question sets","instruction-following task descriptions"],"output_types":["model predictions (text, numerical answers, multiple-choice selections)","accuracy scores per task","aggregate performance metrics (F1, BLEU, exact match)","performance curves for scaling analysis"],"categories":["data-processing-analysis","evaluation-framework"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench__cap_1","uri":"capability://planning.reasoning.scaling.law.extrapolation.analysis","name":"scaling-law-extrapolation-analysis","description":"Enables quantitative analysis of how language model capabilities improve as model size increases by collecting performance data across models of varying scales and fitting scaling curves. The framework supports extrapolation of performance trends to predict capability levels at larger model sizes not yet evaluated, using power-law and other functional forms to model the relationship between model parameters and task performance.","intents":["I want to predict how much better my model will perform if I scale it to 10x or 100x the current size","I need to understand which capabilities improve predictably with scale and which plateau or show diminishing returns","I want to identify capability emergence — tasks where performance jumps suddenly at certain model sizes"],"best_for":["model developers planning compute budgets and training runs for larger models","researchers studying emergent capabilities and scaling laws in language models","organizations deciding whether to invest in larger models vs. architectural improvements"],"limitations":["extrapolation accuracy degrades significantly beyond the training distribution of model sizes tested — predictions for 10T+ parameter models are highly uncertain","assumes scaling follows power-law or similar functional forms, which may break down at extreme scales or for novel architectures","does not account for architectural innovations or training improvements that change the scaling relationship","task saturation effects — some tasks may plateau in performance, making extrapolation meaningless"],"requires":["performance data from multiple models of different sizes (minimum 3-4 scale points recommended)","models spanning at least 1-2 orders of magnitude in parameter count","computational resources to evaluate all models on the full benchmark"],"input_types":["model size (parameter count)","performance scores from benchmark evaluation"],"output_types":["scaling curves (plots of performance vs. model size)","fitted power-law coefficients","extrapolated performance predictions at larger scales","capability emergence analysis"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench__cap_2","uri":"capability://data.processing.analysis.cross.model.capability.comparison","name":"cross-model-capability-comparison","description":"Provides a standardized evaluation framework that enables direct, quantitative comparison of different language models' capabilities on identical tasks with identical metrics. By running multiple models against the same 204-task suite, researchers can generate comparative performance matrices showing which models excel at which capability domains, identify architectural or training differences that lead to capability gaps, and benchmark commercial models against research models.","intents":["I want to objectively compare GPT-3, PaLM, and my own model on the same tasks to see which is strongest at reasoning vs. knowledge","I need to understand whether a new model architecture I developed actually improves on existing approaches or just overfits to specific benchmarks","I want to identify which commercial models are best suited for my use case by comparing their performance on relevant task categories"],"best_for":["model developers comparing their architecture against published baselines","practitioners selecting between commercial LLM APIs based on capability profiles","researchers studying how training data, scale, and architecture interact to produce capability differences"],"limitations":["benchmark results reflect only the 204 tasks included — models may have capabilities not measured by BIG-bench that are important for specific applications","comparison assumes all models are evaluated fairly (same prompting strategy, temperature, etc.), but subtle differences in evaluation setup can significantly impact results","does not measure latency, throughput, or cost-efficiency — only accuracy, so a slower or more expensive model may appear superior","task contamination varies by model — some models may have seen benchmark tasks during training, inflating their scores"],"requires":["access to multiple models to compare (local, API, or research access)","ability to run inference on all models with consistent hyperparameters","computational budget to evaluate all models on all 204 tasks"],"input_types":["model identifiers or instances","benchmark task suite"],"output_types":["performance matrices (model × task)","aggregate capability scores per model","capability profiles (strengths/weaknesses by domain)","statistical significance tests"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench__cap_3","uri":"capability://data.processing.analysis.domain.specific.capability.profiling","name":"domain-specific-capability-profiling","description":"Organizes the 204 benchmark tasks into semantic categories (reasoning, language understanding, code generation, knowledge, instruction-following, bias/toxicity) allowing researchers to generate capability profiles that show model strengths and weaknesses across specific domains. This enables fine-grained analysis of which capability areas a model excels at versus struggles with, supporting targeted model improvement efforts and use-case-specific model selection.","intents":["I need to understand whether my model is better at mathematical reasoning or commonsense reasoning so I can focus improvement efforts","I want to select a model that's strong at code generation but don't care about knowledge tasks, so I need to see capability breakdowns by domain","I need to identify which capability areas are causing my model to underperform and prioritize training improvements"],"best_for":["model developers conducting ablation studies to understand which training approaches improve specific capabilities","practitioners selecting models for domain-specific applications (e.g., code generation, reasoning, knowledge QA)","researchers studying how different model sizes and architectures distribute capability improvements"],"limitations":["task categorization is somewhat subjective — a task like 'reading comprehension with math' could belong to multiple domains, and categorization may not align with user's mental model","domain-level aggregation masks task-level variance — a model might be strong at 90% of reasoning tasks but fail catastrophically on 10%, which is hidden in aggregate scores","does not measure capability transfer or interaction — a model strong at reasoning may not apply that to code generation even though both involve logical thinking","categories are fixed by benchmark design; users cannot define custom capability groupings"],"requires":["benchmark task definitions with domain labels","performance scores for a model on all tasks","understanding of task categorization scheme"],"input_types":["model performance data (per-task scores)","task-to-domain mappings"],"output_types":["capability profiles (domain-level aggregated scores)","capability heatmaps (model × domain)","strength/weakness rankings by domain","task-level performance within domains"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench__cap_4","uri":"capability://automation.workflow.reproducible.evaluation.framework","name":"reproducible-evaluation-framework","description":"Provides open-source task definitions, evaluation code, and metric implementations that enable fully reproducible benchmark evaluation across different research groups and time periods. Tasks are defined as self-contained Python/JSON files with deterministic evaluation logic, allowing any researcher to run identical evaluations and verify published results, supporting scientific reproducibility and preventing benchmark gaming through metric manipulation.","intents":["I want to verify that a published model's BIG-bench results are accurate by running the same evaluation myself","I need to ensure my evaluation is using the exact same metrics and task definitions as the original benchmark to make fair comparisons","I want to contribute new tasks to the benchmark in a way that maintains consistency with existing evaluation methodology"],"best_for":["researchers verifying published results and preventing benchmark gaming","organizations implementing internal evaluation pipelines that must match published benchmarks","contributors adding new tasks to BIG-bench who need to follow standardized evaluation patterns"],"limitations":["reproducibility is limited by randomness in model inference (temperature, sampling) — identical prompts may produce different outputs across runs unless temperature is set to 0","evaluation code is deterministic but model behavior is not — same model weights may produce different outputs due to floating-point non-determinism or stochastic decoding","task definitions are fixed; users cannot modify prompts or evaluation criteria without creating a non-standard variant","reproducibility requires access to the same model weights/API, which may not be available for commercial models"],"requires":["Python 3.7+ environment","access to BIG-bench task definitions (GitHub repository)","the model to evaluate (local weights or API access)","computational resources to run inference"],"input_types":["task definitions (JSON/Python)","model outputs (text, predictions)"],"output_types":["evaluation metrics (accuracy, F1, BLEU, etc.)","per-task scores","aggregate benchmark scores","evaluation logs for debugging"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench__cap_5","uri":"capability://tool.use.integration.collaborative.task.contribution.system","name":"collaborative-task-contribution-system","description":"Enables researchers to contribute new benchmark tasks following standardized templates and validation criteria, allowing the benchmark to grow and evolve with the research community. Contributors submit tasks with input-output examples, evaluation metrics, and difficulty assessments; submissions are reviewed for quality, diversity, and alignment with benchmark goals before inclusion in the official suite.","intents":["I have a new capability I want to measure in language models but it's not covered by existing benchmarks, so I want to contribute a task","I want to ensure my new task meets quality standards and is compatible with the existing benchmark before publishing","I want to help the community by adding tasks that measure emerging capabilities like multimodal reasoning or long-context understanding"],"best_for":["researchers identifying capability gaps in existing benchmarks and designing new tasks to fill them","organizations wanting to contribute domain-specific tasks (e.g., medical reasoning, legal analysis) to the benchmark","benchmark maintainers curating high-quality tasks and preventing low-quality or redundant submissions"],"limitations":["contribution process is manual and requires community review, creating bottlenecks — new tasks may take weeks/months to be accepted","no formal specification for what makes a 'good' task — acceptance criteria are somewhat subjective and may vary by reviewer","contributors must follow existing task templates and evaluation patterns, limiting innovation in how capabilities are measured","no guarantee that contributed tasks will be widely adopted or used in future evaluations"],"requires":["understanding of BIG-bench task format and evaluation methodology","ability to define clear input-output examples and evaluation metrics","willingness to iterate on feedback from reviewers","GitHub account and familiarity with pull request workflow"],"input_types":["task definition (prompt template, examples)","evaluation metric specification","difficulty assessment","capability category classification"],"output_types":["accepted task added to benchmark suite","task metadata (difficulty, category, examples)","evaluation code for the task"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench__cap_6","uri":"capability://safety.moderation.bias.and.toxicity.evaluation.suite","name":"bias-and-toxicity-evaluation-suite","description":"Includes a subset of tasks specifically designed to measure model biases, toxicity, and alignment issues across demographic groups and sensitive topics. These tasks evaluate whether models generate harmful content, exhibit gender/racial/religious biases, or fail to refuse inappropriate requests, providing quantitative metrics for model safety and fairness assessment.","intents":["I need to measure whether my model exhibits gender or racial bias before deploying it to production","I want to understand which demographic groups my model performs worse on and where fairness issues exist","I need to assess whether my model appropriately refuses harmful requests or generates toxic content"],"best_for":["model developers conducting safety and fairness audits before deployment","organizations subject to regulatory requirements for bias and toxicity assessment","researchers studying how model size, training data, and RLHF affect bias and safety"],"limitations":["bias measurement is inherently subjective — what constitutes 'bias' varies by cultural context and values, and BIG-bench's definitions may not align with all stakeholders","toxicity evaluation relies on keyword matching or classifier-based detection, which can miss subtle harmful content or flag benign text as toxic","bias tasks may not cover all relevant demographic dimensions or intersectional biases","evaluation does not measure real-world harms or downstream impacts — a model might score well on bias metrics but still cause harm in deployment"],"requires":["understanding of fairness and safety concepts","willingness to interpret results in context of model's intended use case","awareness that quantitative bias metrics are incomplete proxies for actual fairness"],"input_types":["prompts designed to elicit biased or toxic responses","demographic group identifiers (gender, race, religion, etc.)","sensitive topic categories"],"output_types":["bias scores per demographic group","toxicity detection results","fairness metrics (e.g., performance gap between groups)","safety assessment report"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench__cap_7","uri":"capability://planning.reasoning.instruction.following.capability.measurement","name":"instruction-following-capability-measurement","description":"Includes tasks that evaluate whether models can follow complex, multi-step instructions, understand nuanced task specifications, and adapt behavior based on explicit guidance. These tasks measure instruction-following as a distinct capability from knowledge or reasoning, testing whether models can parse instructions accurately and execute them correctly even when instructions conflict with training patterns.","intents":["I want to measure whether my model can follow complex, multi-step instructions without getting confused or reverting to default behavior","I need to understand how well my model generalizes to novel instruction formats it hasn't seen during training","I want to assess whether my model's instruction-following improves with RLHF or instruction-tuning"],"best_for":["model developers evaluating instruction-tuning and RLHF effectiveness","researchers studying how models learn to follow instructions and generalize to novel formats","practitioners selecting models for applications requiring precise instruction adherence (e.g., API-like behavior)"],"limitations":["instruction-following is highly dependent on prompt format and phrasing — small changes in how instructions are written can dramatically affect performance","evaluation assumes a single 'correct' interpretation of instructions, but ambiguous instructions may have multiple valid interpretations","does not measure instruction-following in interactive settings where models can ask clarifying questions","task set may not cover all instruction types (e.g., visual instructions, multi-modal instructions)"],"requires":["models with instruction-tuning or RLHF training","understanding of instruction-following as a distinct capability"],"input_types":["complex, multi-step instructions","novel instruction formats","instructions with constraints or edge cases"],"output_types":["instruction-following accuracy scores","error analysis (types of instruction misunderstandings)","generalization metrics (performance on novel instruction formats)"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":23,"verified":false,"data_access_risk":"high","permissions":["a language model to evaluate (local or via API)","computational resources to run inference (GPU recommended for efficiency)","Python 3.7+ to load and execute benchmark tasks","understanding of evaluation metrics and statistical interpretation","performance data from multiple models of different sizes (minimum 3-4 scale points recommended)","models spanning at least 1-2 orders of magnitude in parameter count","computational resources to evaluate all models on the full benchmark","access to multiple models to compare (local, API, or research access)","ability to run inference on all models with consistent hyperparameters","computational budget to evaluate all models on all 204 tasks"],"failure_modes":["204 tasks, while broad, cannot comprehensively cover all real-world use cases or domain-specific requirements","evaluation metrics are task-dependent and some use proxy metrics (BLEU for generation) rather than human judgment, potentially missing nuanced capability differences","no built-in handling of task contamination — benchmark tasks may overlap with model training data, inflating performance estimates","requires user to supply and run their own LLM inference; benchmark provides no inference service","extrapolation accuracy degrades significantly beyond the training distribution of model sizes tested — predictions for 10T+ parameter models are highly uncertain","assumes scaling follows power-law or similar functional forms, which may break down at extreme scales or for novel architectures","does not account for architectural innovations or training improvements that change the scaling relationship","task saturation effects — some tasks may plateau in performance, making extrapolation meaningless","benchmark results reflect only the 204 tasks included — models may have capabilities not measured by BIG-bench that are important for specific applications","comparison assumes all models are evaluated fairly (same prompting strategy, temperature, etc.), but subtle differences in evaluation setup can significantly impact results","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.31,"ecosystem":0.25,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"inactive","updated_at":"2026-06-17T09:51:02.371Z","last_scraped_at":"2026-05-03T14:00:27.894Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench","compare_url":"https://unfragile.ai/compare?artifact=beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench"}},"signature":"JnRPXgtAizPsNFdDb8brcvC6gL6f+2xz7GeAVt00VqT38j+9QUrENAM5TuyqW9hT79I8UCsOEeM1pSDmeOKkAg==","signedAt":"2026-06-20T01:08:21.294Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench","artifact":"https://unfragile.ai/beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench","verify":"https://unfragile.ai/api/v1/verify?slug=beyond-the-imitation-game-quantifying-and-extrapolating-the-capabilities-of-lang-big-bench","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}