HellaSwag: Commonsense Event Continuation

Reasoning

Tests models’ commonsense reasoning abilities by asking them to select the most likely next step or continuation for a given everyday situation.

Contributed By

@jjallaire

Code

src/inspect_evals/hellaswag

Paper

https://arxiv.org/abs/1905.07830

Overview

HellaSwag is a dataset for commonsense inference. The model is prompted with a sentence and the model is tasked to pick the sentence choice that is the best suited continuation.

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/hellaswag --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.hellaswag import hellaswag
eval(hellaswag)

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

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

Parameters

hellaswag

  • shuffle (bool): (default: False)
  • split (Literal[‘train’, ‘validation’, ‘test’]): (default: 'validation')

Dataset

Here is an example prompt from the dataset (after it has been further processed by Inspect):

Choose the most plausible continuation for the story.

Answer the following multiple choice question. The entire content of your response should be of the following format: ‘ANSWER: $LETTER’ (without quotes) where LETTER is one of A,B,C,D.

A man is sitting on a roof. he

  1. is using wrap to wrap a pair of skis.
  2. is ripping level tiles off.
  3. is holding a rubik’s cube.
  4. starts pulling up roofing on a roof.

The model is then expected to generate reasoning steps and provide a final answer.

Scoring

An accuracy is calculated over the datapoints.