# 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://metaverse-imagen.gitbook.io/ai-tools-research/about-ai-tools-research/llm-performance-benchmarks/llm-benchmarks-and-tasks/triviaqa-5-shot.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.