Language feature score helps analyze how well the language used in a response conveys the intended message, whether it addresses the question or issue comprehensively, and if it is free from ambiguity or confusion.

Columns required:

  • response: The response given by the model

​
How to use it?

from uptrain import EvalLLM

OPENAI_API_KEY = "sk-********************"  # Insert your OpenAI key here

data = [{
    "response": "hey, so quadratic equation solving, I will guide you! just refer to any book on basic algebra it is pretty straightforward, even a dummy can understand."
}]

eval_llm = EvalLLM(openai_api_key=OPENAI_API_KEY)

res = eval_llm.evaluate(
    data = data,
    checks = [Evals.CRITIQUE_LANGUAGE]    
)
By default, we are using GPT 3.5 Turbo for evaluations. If you want to use a different model, check out this tutorial.

Sample Response:

[
   {
      "score_fluency": 0.4,
      "score_coherence": 0.4,
      "score_grammar": 0.4,
      "score_politeness": 0.2,
      "explanation_fluency": "The text is not fluent and sounds awkward due to the informal language and lack of proper structure.",
      "explanation_coherence": "The text lacks coherence as it jumps between different topics without a clear connection.",
      "explanation_grammar": "The text contains grammatical errors and informal language that is not suitable for a professional or academic setting.",
      "explanation_politeness": "The tone is impolite and condescending, using the term \"dummy\" which is inappropriate."
   }
]
Higher language features scores reflects a good response.

The reponse generated does not seem good, it has innapropriate words like “dummy”, there are some grammatical errors and uses unnecessary slangs like: “I will guide you”, “it is pretty straightforward”

Resulting in low language feature scores.

​
How it works?

We evaluate language features by determining which of the following three cases apply for the given task data across features such as fluent, polite, grammatically correct, and coherent:

  • The response is highly rated on these features.
  • The response is moderately rated on these features.
  • The response is poorly rated on these features.

Tutorial

Open this tutorial in GitHub

Have Questions?

Join our community for any questions or requests

Language feature score helps analyze how well the language used in a response conveys the intended message, whether it addresses the question or issue comprehensively, and if it is free from ambiguity or confusion.

Columns required:

  • response: The response given by the model

​
How to use it?

from uptrain import EvalLLM

OPENAI_API_KEY = "sk-********************"  # Insert your OpenAI key here

data = [{
    "response": "hey, so quadratic equation solving, I will guide you! just refer to any book on basic algebra it is pretty straightforward, even a dummy can understand."
}]

eval_llm = EvalLLM(openai_api_key=OPENAI_API_KEY)

res = eval_llm.evaluate(
    data = data,
    checks = [Evals.CRITIQUE_LANGUAGE]    
)
By default, we are using GPT 3.5 Turbo for evaluations. If you want to use a different model, check out this tutorial.

Sample Response:

[
   {
      "score_fluency": 0.4,
      "score_coherence": 0.4,
      "score_grammar": 0.4,
      "score_politeness": 0.2,
      "explanation_fluency": "The text is not fluent and sounds awkward due to the informal language and lack of proper structure.",
      "explanation_coherence": "The text lacks coherence as it jumps between different topics without a clear connection.",
      "explanation_grammar": "The text contains grammatical errors and informal language that is not suitable for a professional or academic setting.",
      "explanation_politeness": "The tone is impolite and condescending, using the term \"dummy\" which is inappropriate."
   }
]
Higher language features scores reflects a good response.

The reponse generated does not seem good, it has innapropriate words like “dummy”, there are some grammatical errors and uses unnecessary slangs like: “I will guide you”, “it is pretty straightforward”

Resulting in low language feature scores.

​
How it works?

We evaluate language features by determining which of the following three cases apply for the given task data across features such as fluent, polite, grammatically correct, and coherent:

  • The response is highly rated on these features.
  • The response is moderately rated on these features.
  • The response is poorly rated on these features.

Tutorial

Open this tutorial in GitHub

Have Questions?

Join our community for any questions or requests