Hallucination

The hallucination metric uses LLM-as-a-judge to determine whether your LLM generates factually correct information by comparing the actual_output to the provided context.

info

If you're looking to evaluate hallucination for a RAG system, please refer to the faithfulness metric instead.

Required Arguments

To use the HallucinationMetric, you'll have to provide the following arguments when creating an LLMTestCase:

• input
• actual_output
• context

The input and actual_output are required to create an LLMTestCase (and hence required by all metrics) even though they might not be used for metric calculation. Read the How Is It Calculated section below to learn more.

Example

from deepeval import evaluate
from deepeval.metrics import HallucinationMetric
from deepeval.test_case import LLMTestCase

# Replace this with the actual documents that you are passing as input to your LLM.
context=["A man with blond-hair, and a brown shirt drinking out of a public water fountain."]

# Replace this with the actual output from your LLM application
actual_output="A blond drinking water in public."

test_case = LLMTestCase(
    input="What was the blond doing?",
    actual_output=actual_output,
    context=context
)
metric = HallucinationMetric(threshold=0.5)

# To run metric as a standalone
# metric.measure(test_case)
# print(metric.score, metric.reason)

evaluate(test_cases=[test_case], metrics=[metric])

There are SIX optional parameters when creating a HallucinationMetric:

• [Optional] threshold: a float representing the maximum passing threshold, defaulted to 0.5.
• [Optional] model: a string specifying which of OpenAI's GPT models to use, OR any custom LLM model of type DeepEvalBaseLLM. Defaulted to 'gpt-4o'.
• [Optional] include_reason: a boolean which when set to True, will include a reason for its evaluation score. Defaulted to True.
• [Optional] strict_mode: a boolean which when set to True, enforces a binary metric score: 0 for perfection, 1 otherwise. It also overrides the current threshold and sets it to 0. Defaulted to False.
• [Optional] async_mode: a boolean which when set to True, enables concurrent execution within the measure() method. Defaulted to True.
• [Optional] verbose_mode: a boolean which when set to True, prints the intermediate steps used to calculate said metric to the console, as outlined in the How Is It Calculated section. Defaulted to False.

As a standalone

You can also run the HallucinationMetric on a single test case as a standalone, one-off execution.

...

metric.measure(test_case)
print(metric.score, metric.reason)

caution

This is great for debugging or if you wish to build your own evaluation pipeline, but you will NOT get the benefits (testing reports, Confident AI platform) and all the optimizations (speed, caching, computation) the evaluate() function or deepeval test run offers.

How Is It Calculated?

The HallucinationMetric score is calculated according to the following equation:

$$\text{Hallucination} = \frac{\text{Number of Contradicted Contexts}}{\text{Total Number of Contexts}}$$

The HallucinationMetric uses an LLM to determine, for each context in contexts, whether there are any contradictions to the actual_output.

info

Although extremely similar to the FaithfulnessMetric, the HallucinationMetric is calculated differently since it uses contexts as the source of truth instead. Since contexts is the ideal segment of your knowledge base relevant to a specific input, the degree of hallucination can be measured by the degree of which the contexts is disagreed upon.