> For the complete documentation index, see [llms.txt](https://metaverse-imagen.gitbook.io/ai-tools-research/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://metaverse-imagen.gitbook.io/ai-tools-research/about-ai-tools-research/llm-performance-benchmarks/llm-benchmarks-and-tasks/triviaqa-5-shot.md).

# TriviaQA (5-Shot)

**TriviaQA (5-Shot) is a Benchmark Dataset**

TriviaQA (5-Shot) is a benchmark dataset for evaluating the performance of large language models (LLMs) on a variety of factual question-answering tasks. 

The dataset consists of over 200,000 questions and answers, which are divided into 5 subsets, each of which contains 40,000 questions. 

The questions are designed to be challenging and require the LLM to have a deep understanding of the world in order to answer them correctly.

The 5-Shot setting in TriviaQA means that the LLM is given 5 examples of each question type before it is asked to answer the question. This is done to ensure that the LLM has a good understanding of the question type and the answer format before it is asked to answer the question.

The TriviaQA (5-Shot) benchmark is a popular way to compare the performance of different LLMs on factual question-answering tasks. It is a challenging benchmark, but it is also a good measure of the overall capabilities of an LLM.

Some of the specific tasks that the TriviaQA (5-Shot) benchmark evaluates include:

* **Answering factual questions about the world:** For example, "What is the capital of France?"
* **Answering questions about common sense knowledge:** For example, "What is the best way to get to the airport?"
* **Answering questions about relationships between entities:** For example, "What is the relationship between Barack Obama and Joe Biden?"
* **Answering questions about events:** For example, "When did the American Civil War start?"

The TriviaQA (5-Shot) benchmark is a valuable tool for researchers and developers who are interested in evaluating the performance of LLMs on factual question-answering tasks.