TriviaQA is a benchmark consisting of fact-seeking questions with short answers, measuring model accuracy for attempted responses.
This test uses the testing set of TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension, which consists of 9,280 fact-seeking questions with short answers, measuring model accuracy for attempted responses.
This test evaluates a model’s ability to provide short, factual answers or explicit acknowledgment of a lack of knowledge when uncertain. The evaluation process consists of two steps. First, a judge determines whether the response includes an explicit acknowledgment of a lack of information (e.g., "I don't know"). Second, we check the factual correctness of responses. The TriviaQA Score is calculated as the percentage of responses that are either factually correct or properly acknowledge uncertainty. This methodology ensures that models are evaluated based on their ability to provide reliable and succinct factual information while minimizing the spread of incorrect or misleading statements.