BIG-Bench is a comprehensive benchmark for evaluating the capabilities and biases of large language models (LLMs). It is designed to be more challenging and long-lasting than previous benchmarks and to provide a more holistic understanding of LLM performance.

BIG-Bench can be applied to various domains and fields that involve the use of LLMs, such as natural language processing, computer vision, speech recognition, and natural language generation. Some specific areas of application are:

• AI Education: BIG-Bench can be used to teach and test the skills of AI students and practitioners. It can help them learn how to design, implement, and evaluate AI systems that can reason and use knowledge to answer questions from various domains.
• AI Evaluation: BIG-Bench can be used to measure and compare the capabilities of different AI systems, especially LLMs. It can provide a comprehensive and challenging evaluation of how well these systems can perform on tasks that require reasoning and knowledge.
• AI Innovation: BIG-Bench can be used to inspire and motivate new research and development in AI. It can provide a platform for exploring new ideas, methods, and applications for AI systems that can reason and use knowledge.

Some examples of tasks and subtasks that are included in BIG-Bench are:

• Chess: This task tests the ability of LLMs to play chess and answer questions about chess rules and strategies. It includes subtasks such as chess notation, chess puzzles, and chess commentary.
• Emoji: This task tests the ability of LLMs to understand and generate emoji. It includes subtasks such as emoji guessing, emoji translation, and emoji story.
• Social Bias: This task tests the degree of social bias present in LLMs and the effectiveness of mitigation strategies. It includes subtasks such as gender bias, racial bias, and religious bias.

• BIG-bench – GitHub
• BIG-Bench: The New Benchmark for Language Models | Deepgram
• BIG-bench Dataset | Papers With Code