**kwargs: Any,
    ) -> CriteriaEvalChain:
        """Create a `LabeledCriteriaEvalChain` instance from an llm and criteria.
        Parameters
        ----------
        llm : BaseLanguageModel
            The language model to use for evaluation.
        criteria : CRITERIA_TYPE - default=None for "helpfulness"
            The criteria to evaluate the runs against. It can be:
                -  a mapping of a criterion name to its description
                -  a single criterion name present in one of the default criteria
                -  a single `ConstitutionalPrinciple` instance
        prompt : Optional[BasePromptTemplate], default=None
            The prompt template to use for generating prompts. If not provided,
            a default prompt will be used.
        **kwargs : Any
            Additional keyword arguments to pass to the `LLMChain`
            constructor.
        Returns
        -------
        LabeledCriteriaEvalChain
            An instance of the `LabeledCriteriaEvalChain` class.
        Examples
        --------
        >>> from langchain.llms import OpenAI
        >>> from langchain.evaluation.criteria import LabeledCriteriaEvalChain
        >>> llm = OpenAI()
        >>> criteria = {
                "hallucination": (
                    "Does this submission contain information"
                    " not present in the input or reference?"
                ),
            }
        >>> chain = LabeledCriteriaEvalChain.from_llm(
                llm=llm,
                criteria=criteria,
            )
        """
        prompt = cls._resolve_prompt(prompt)
        criteria_ = cls.resolve_criteria(criteria)
        criteria_str = "\n".join(f"{k}: {v}" for k, v in criteria_.items())
        prompt_ = prompt.partial(criteria=criteria_str)
        return cls(