CommonsenseQA is a benchmark dataset for evaluating the commonsense reasoning capabilities of artificial intelligence systems. Released in 2019 by researchers at the University of Washington and the Allen Institute for Artificial Intelligence, it consists of 12,247 multiple-choice questions, each with one correct answer and four distractor options. The dataset was designed to address a gap in existing natural language processing benchmarks, which often focused on factual recall or syntactic understanding but did not adequately test a model's ability to apply everyday knowledge about the world.
The questions in CommonsenseQA are generated from the ConceptNet knowledge graph, a large semantic network that encodes relationships between everyday concepts. Each question is based on a triple from ConceptNet, such as 'a dog is an animal' or 'you use an umbrella when it rains'. The dataset creators used crowdsourced workers to transform these triples into natural language questions and to generate plausible but incorrect answer choices. This process ensures that the questions require genuine commonsense reasoning rather than simple pattern matching or memorization.
Construction and Design
The creation of CommonsenseQA involved a multi-stage pipeline. First, the researchers selected a set of ConceptNet triples that involved common, everyday concepts. They then used Amazon Mechanical Turk to have workers write questions that would test whether a system could infer the correct relationship from the triple. For each question, workers also generated four distractor answers that were semantically related to the correct answer but incorrect in the context of the question. This adversarial design makes the benchmark challenging, as the distractors are often plausible but wrong.
The final dataset was split into training, development, and test sets. The training set contains 9,741 questions, the development set contains 1,221 questions, and the test set contains 1,241 questions. The test set is not publicly released, and researchers must submit their models to an evaluation server to obtain results on it, which helps prevent overfitting.
Evaluation and Impact
CommonsenseQA quickly became a standard benchmark for evaluating large language models and other AI systems. Early results showed that even state-of-the-art models at the time, such as BERT and GPT-2, performed only slightly better than random guessing, which is 20% accuracy for a five-option multiple-choice task. This highlighted the difficulty of commonsense reasoning for AI systems.
Subsequent work improved performance significantly. Models that incorporated external knowledge, such as those that retrieved relevant ConceptNet triples or used graph neural networks, achieved higher accuracy. The introduction of larger pre-trained models, including GPT-3 and later models, also led to substantial gains. By 2021, some models were approaching 80% accuracy on the development set, though human performance is estimated at around 91%.
Relationship to Other Benchmarks
CommonsenseQA is part of a broader family of commonsense reasoning benchmarks, including Winograd Schema Challenge, SWAG, and HellaSwag. Unlike these benchmarks, which focus on pronoun resolution or sentence completion, CommonsenseQA explicitly tests multiple-choice question answering with a diverse set of everyday scenarios. It has been used in conjunction with other datasets to evaluate general-purpose AI systems, including those developed by companies such as OpenAI, Anthropic, and Google DeepMind.
The dataset has also been extended and adapted. For example, a variant called CommonsenseQA 2.0 was released in 2021, which uses a different question generation approach based on generative models. Additionally, researchers have created adversarial splits of CommonsenseQA to test model robustness, where questions are modified to make them harder for models that rely on superficial cues.
Limitations and Criticisms
Despite its popularity, CommonsenseQA has faced criticism. Some researchers have noted that the dataset contains biases, such as a tendency for certain answer options to be more frequent or for the correct answer to be longer or more specific. Models can exploit these statistical regularities without performing true reasoning. Additionally, the questions are all in English and reflect a Western-centric view of commonsense, which limits its applicability to other cultures and languages.
Another limitation is that the dataset is static, meaning it does not capture the evolving nature of commonsense knowledge. As the world changes, what is considered common sense may shift, but the benchmark remains fixed. This has led some researchers to call for more dynamic and diverse evaluation methods.
Current Usage
As of the mid-2020s, CommonsenseQA remains a widely used benchmark in the Artificial intelligence and Machine learning research community. It is often included in the evaluation suites of large language models, alongside other benchmarks such as MMLU and BIG-bench. The dataset is freely available for research purposes and is hosted on platforms like Hugging Face, making it easy for researchers to access and use.
CommonsenseQA has also been used to study the reasoning abilities of models in more detail. For example, researchers have analyzed which types of commonsense knowledge (e.g., spatial, temporal, social) are most challenging for models, and have used the dataset to probe the internal representations of Transformer (architecture)-based models. This ongoing research continues to inform the development of more robust and capable AI systems.