Readability

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Objective: The Readability metric evaluates whether the generated response flows well linguistically and is easy to understand by checking the paragraph-level grade score. Lower scores (e.g., 6-8) generally indicate easier readability with less complex words, while higher scores (e.g., 12+) suggest the use of more complex words. This metric is useful for determining if the complexity of a LinkedIn post is suitable for the intended target audience and for identifying whether any particular paragraph’s readability is impacting the overall flow of the post.

Required Columns in Dataset:

• Prompt: The original request or topic that led to the creation of the LinkedIn post.

• Content: The main body of the LinkedIn post.

• Response: The LinkedIn post generated by the model that is being evaluated for readability.

Score Range: Typically 0 to 14+, corresponding to U.S. grade levels

Interpretation: The specific interpretation of the readability score may vary depending on the target audience and the context of the post. For example, a lower grade level score may be desirable for a broader, general audience, while a higher grade level score may be appropriate for a more specialized or academic audience.

Code Implementation

pythonCopy codeexperiment_manager = Experiment(project_name="project_name",
                                experiment_name="experiment_name",
                                experiment_description="",
                                dataset_name="dataset_name")

response = experiment_manager.add_metrics(
    metrics=[
        {"name":"Readability", "config": {"reason": True, "model": "gpt-4o-mini", "batch_size" : 5, "provider": "OpenAI"}}
    ]
)

print("Metric Response:", response)

Refer Executing tests page to learn about Metric Configurations

Example :

Prompt: Write a LinkedIn post about the benefits of data-driven decision-making.

Content: Data-driven decision-making is a crucial process in modern business environments, enabling companies to leverage quantitative insights for strategic advantages. By systematically analyzing datasets, businesses can ascertain trends, predict outcomes, and optimize operations to achieve superior performance. This approach, however, necessitates an advanced comprehension of data analytics and a robust infrastructure to manage and interpret large volumes of data.

Response: Title: The Power of Data-Driven Decisions In today's fast-paced business world, leveraging data for decision-making is not just a trend but a necessity. Companies that embrace data-driven strategies can predict trends, optimize operations, and achieve a competitive edge. However, this requires a deep understanding of data analytics and the right tools to manage vast amounts of information.

Metric Score: Score: 12.5/14+

Reasoning:

• Complexity: The post contains complex words and concepts, such as "quantitative insights," "strategic advantages," and "advanced comprehension of data analytics," leading to a high readability score.

• Target Audience: The high score suggests that the content is more suitable for an audience with a strong background in data analytics or business strategy, possibly at an executive or managerial level.

• Flow: The complexity of the language may impact the overall flow for readers who are less familiar with these concepts, making the post less accessible to a general audience.

Interpretation: The high readability score indicates that the post uses complex language and concepts, which might be ideal for a specialized audience but could be challenging for a broader, more general audience. Adjusting the language to a lower grade level may make the post more accessible, depending on the target audience.