Evals
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Grounded Evals

Grounded Evaluators

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❊ Info

Grounded evaluators are designed to assess the relevance of a response or context based on specific similarity algorithm.

How does it work

Grounded evaluators compare a given response to a reference or context, using various similarity measures to determine the degree of relevance or similarity.

Required Args

Your dataset must contain these fields:

  • response: The LLM generated response.
  • expected_response: The reference content to compare the response against in case of AnswerSimilarity.
  • context: The reference content to compare the response against in case of ContextSimilarity.

Metrics

  • SimilarityScore: A numeric value representing the degree of similarity or relevance.

▷ Run the AnswerSimilarity evaluator on a single datapoint

from athina.evals import AnswerSimilarity
from athina.evals.grounded.similarity import CosineSimilarity
 
# Checks the similarity between the response and the reference answer
response = "The capital of France is Paris."
expected_response = "Paris is the capital of France."
AnswerSimilarity(comparator=CosineSimilarity()).run(response=response, expected_response=expected_response).to_df()

▷ Run the function eval on a dataset

  1. Load your data with the Loader
from athina.loaders import Loader
raw_data = [
    {
        "response": "Thomas Edison invented the light bulb.",
        "expected_response": "The light bulb was invented by Thomas Edison."
    },
    {
        "response":  "The capital of France is Paris.",
        "expected_response": "Paris is the capital of France."
    }
]
# Load the data from CSV, JSON, Athina or Dictionary
dataset = Loader().load_dict(raw_data)
  1. Run the evaluator on your dataset
from athina.evals import AnswerSimilarity
from athina.evals.grounded.similarity import CosineSimilarity
 
# Evaluates the similarity of the response to the expected response
AnswerSimilarity(comparator=CosineSimilarity()).run_batch(data=dataset).to_df()

Following are examples of the various Grounded evaluators we support

AnswerSimilarity

Description: Evaluates the similarity between the generated response and a given expected response.

Arguments:

  • comparator: Comparator The similarity measurement function (e.g., CosineSimilarity).
  • failure_threshold: float The threshold value for determining pass/fail.

Sample Code:

from athina.evals.grounded import AnswerSimilarity
from athina.evals.grounded.similarity import CosineSimilarity
 
AnswerSimilarity(comparator=CosineSimilarity(), failure_threshold=0.75).run_batch(data=dataset).to_df()

ContextSimilarity

Description: Evaluates the similarity between the generated response and the context.

Arguments:

  • comparator: Comparator The similarity measurement function (e.g., CosineSimilarity).
  • failure_threshold: float The threshold value for determining pass/fail.

Sample Code:

from athina.evals.grounded import ContextSimilarity
from athina.evals.grounded.similarity import CosineSimilarity
 
ContextSimilarity(comparator=CosineSimilarity(), failure_threshold=0.75).run_batch(data=dataset).to_df()
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