BIG-Bench

LLMs Explained,
Big Bench

The Big Bench model is a benchmarking tool developed to evaluate the performance of large-scale language models (LSLMs) on a range of natural language processing (NLP) tasks. A team of researchers introduced the Big Bench at the University of California, Berkeley, in 2021. The paper introduces Big bench, a benchmark that assesses the capabilities and limitations of large-scale language models across a wide range of diverse and complex tasks. BIG-bench includes 204 tasks across different domains, including math, physics, linguistics, and social bias. The benchmark evaluates various language models, including OpenAI’s GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers.

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An Overview of BIG-Bench

Big-Bench benchmark tool measures model performance using a metric called Task-Tuned Score (TTS), which is computed based on the model's accuracy on specific tasks. The Big Bench is a significant advancement in evaluating LSLMs, providing a standardized and comprehensive evaluation of their capabilities across a wide range of NLP tasks.

Model have large-scale dataset of 15 terabytes

15 terrabytes dataset

The Big-Bench benchmark uses a large-scale dataset consisting of over 15 terabytes of text data from various sources, including common crawl and scientific papers to answer questions related to various fields of study.

Consists of 204 tasks by various authors

444 author contribution

BIG-bench currently consists of 204 tasks contributed by 444 authors across 132 institutions, which include drawing problems from linguistics, math, common-sense reasoning, social bias, and beyond.

Handles extremely diverse and difficult tasks

200 diverse tasks

The original paper's authors announced BIG-bench, a comprehensive benchmark to evaluate the performance of language models on over 200 challenging and diverse tasks.

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Introduction to BIG-Bench

Language models have succeeded remarkably in various natural language processing (NLP) tasks. However, their capabilities and limitations still need to be fully understood. To address this gap, the authors introduce BIG-bench, which evaluates the performance of language models on tasks that are believed to be beyond their current capabilities. The benchmark aims to inform future research, prepare for disruptive new model capabilities, and reduce the potential for socially harmful effects. The Big Bench consists of 200 diverse tasks that cover a range of NLP applications, including text generation, summarization, translation, and question-answering. Each task is carefully designed to represent a specific NLP task or challenge and requires different skills from the evaluated LSLM.

Model highlights

Following are the key highlights of the Big-Bench model.

  • BIG-Bench is a benchmarking tool to evaluate language models' present and near-future capabilities and limitations.
  • BIG-bench evaluates the behavior of different model classes, including OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers.
  • Model performance and calibration improve with scale but are poor in absolute terms and when compared with rater performance.
  • Performance is remarkably similar across model classes, though with benefits from sparsity.
  • Tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps, components, or brittle metrics.
  • Social bias typically increases with scale in settings with ambiguous contexts, but this can be improved with prompting.
Language ModelingIntent recognition:
Text completion and predictionCustomer service chatbots
Sentiment analysisVoice assistants
Text classificationSales and marketing automation
Language translationFraud detection and prevention
Content generation and summarizationCustomer feedback analysis
Speech recognition and transcriptionMarket research and customer profiling
Personalization and recommendation systemsHealth and wellness coaching
Information retrieval and search enginesEducational and training chatbots
Fraud detection and spam filtering.E-commerce product recommendations.
Non-Emb. ParamsFLOP eq.nlayersdmodel dfnheadsnkvnmoenexperts
51M3M 125620484128132
212M18M251240968128132
495M60M3768614412128132
1.7B147M41024819216128232
2.7B282M 512801024020128232
3.9B481M 615361228824128232
7.3B1.1B 820481638432 128232
11.8B2.2B1025602048040128332
24.7B3.8B1230722457648128332
46.0B8.9B1640963276864128432