CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge

Evaluates an AI model’s ability to correctly answer everyday questions that rely on basic commonsense knowledge and understanding of the world.

Overview

CommonsenseQA is a dataset designed to evaluate commonsense reasoning capabilities in natural language processing models. It consists of 12,247 multiple-choice questions that require background knowledge and commonsense to answer correctly. The dataset was constructed using CONCEPTNET, a graph-based knowledge base, where crowd-workers authored questions with complex semantics to challenge existing AI models.

Usage

Installation

There are two ways of using Inspect Evals, from pypi as a dependency of your own project and as a standalone checked out GitHub repository.

If you are using it from pypi, install the package and its dependencies via:

pip install inspect-evals

If you are using Inspect Evals in its repository, start by installing the necessary dependencies with:

uv sync

Running evaluations

Now you can start evaluating models. For simplicity’s sake, this section assumes you are using Inspect Evals from the standalone repo. If that’s not the case and you are not using uv to manage dependencies in your own project, you can use the same commands with uv run dropped.

uv run inspect eval inspect_evals/commonsense_qa --model openai/gpt-5-nano

You can also import tasks as normal Python objects and run them from python:

from inspect_ai import eval
from inspect_evals.commonsense_qa import commonsense_qa
eval(commonsense_qa)

After running evaluations, you can view their logs using the inspect view command:

uv run inspect view

For VS Code, you can also download Inspect AI extension for viewing logs.

If you don’t want to specify the --model each time you run an evaluation, create a .env configuration file in your working directory that defines the INSPECT_EVAL_MODEL environment variable along with your API key. For example:

INSPECT_EVAL_MODEL=anthropic/claude-opus-4-1-20250805
ANTHROPIC_API_KEY=<anthropic-api-key>

Options

You can control a variety of options from the command line. For example:

uv run inspect eval inspect_evals/commonsense_qa --limit 10
uv run inspect eval inspect_evals/commonsense_qa --max-connections 10
uv run inspect eval inspect_evals/commonsense_qa --temperature 0.5

See uv run inspect eval --help for all available options.

Parameters

commonsense_qa

• shuffle (bool): (default: True)

Dataset

CommonsenseQA is a multiple-choice question answering dataset with 1,140 samples which require different types of commonsense knowledge to predict the correct answers. Here is an example from the dataset:

Where can I stand on a river to see water falling without getting wet?

1. Waterfall
   
2. Bridge
   
3. Valley
   
4. Stream
   
5. Bottom

The model is required to choose the correct answer from the given options.

Scoring

A simple accuracy is calculated over the datapoints.