bench-dataset-evaluator-question

name	bench-dataset-evaluator-question
description	Reference documentation for the BenchDatasetEvaluatorQuestion operator. Extended version of BenchDatasetEvaluator with question and subquestion support. Use when: evaluating answers with question context or multiple subquestions.

BenchDatasetEvaluatorQuestion Operator Reference

BenchDatasetEvaluatorQuestion extends BenchDatasetEvaluator with support for question context and subquestions.

1. Import

from dataflow.operators.core_text import BenchDatasetEvaluatorQuestion

2. Match Mode

Constructor

BenchDatasetEvaluatorQuestion(
    eval_result_path=None,
    compare_method="match",
)

Parameter	Required	Default	Description
eval_result_path	No	Auto-generated	Path to save evaluation statistics
compare_method	No	"match"	Must be "match"
system_prompt	No	"You are a helpful assistant..."	Not used in match mode
llm_serving	No	None	Not used in match mode
prompt_template	No	AnswerJudgePromptQuestion	Not used in match mode

run() Signature

op.run(
    storage=self.storage.step(),
    input_question_key="question",
    input_test_answer_key="generated_cot",
    input_gt_answer_key="golden_answer",
)
# returns: list of column names

Parameter	Required	Default	Description
storage	Yes	None	Storage step object
input_question_key	No	"question"	Question column
input_test_answer_key	No	"generated_cot"	Predicted answer column
input_gt_answer_key	No	"golden_answer"	Ground truth column

Usage Example

from dataflow.operators.core_text import BenchDatasetEvaluatorQuestion
from dataflow.utils.storage import FileStorage

class MyPipeline:
    def __init__(self):
        self.storage = FileStorage(
            first_entry_file_name="./data/bench.jsonl",
            cache_path="./cache",
            file_name_prefix="step",
            cache_type="jsonl"
        )

        self.evaluator = BenchDatasetEvaluatorQuestion(
            compare_method="match",
            eval_result_path="./results/match_eval.json"
        )

    def forward(self):
        self.evaluator.run(
            storage=self.storage.step(),
            input_question_key="question",
            input_test_answer_key="predicted_answer",
            input_gt_answer_key="ground_truth"
        )

if __name__ == "__main__":
    pipeline = MyPipeline()
    pipeline.forward()

3. Semantic Mode

Constructor

BenchDatasetEvaluatorQuestion(
    eval_result_path=None,
    compare_method="semantic",
    system_prompt="You are a helpful assistant specialized in evaluating answer correctness.",
    llm_serving=llm_serving,
    prompt_template=None,
    support_subquestions=False,
)

Parameter	Required	Default	Description
eval_result_path	No	Auto-generated	Path to save evaluation statistics
compare_method	Yes	None	Must be "semantic"
system_prompt	No	"You are..."	System prompt for LLM
llm_serving	Yes	None	LLM service object
prompt_template	No	AnswerJudgePromptQuestion	Pass None to use built-in fallback
support_subquestions	No	False	Enable subquestion evaluation

run() Signature

op.run(
    storage=self.storage.step(),
    input_question_key="question",
    input_test_answer_key="generated_cot",
    input_gt_answer_key="golden_answer",
)
# returns: list of column names

Parameter	Required	Default	Description
storage	Yes	None	Storage step object
input_question_key	No	"question"	Question column
input_test_answer_key	No	"generated_cot"	Predicted answer column
input_gt_answer_key	No	"golden_answer"	Ground truth column

Usage Example

from dataflow.operators.core_text import BenchDatasetEvaluatorQuestion
from dataflow.serving import APILLMServing_request
from dataflow.utils.storage import FileStorage

class MyPipeline:
    def __init__(self):
        self.storage = FileStorage(
            first_entry_file_name="./data/bench.jsonl",
            cache_path="./cache",
            file_name_prefix="step",
            cache_type="jsonl"
        )

        self.llm_serving = APILLMServing_request(
            api_url="https://api.openai.com/v1/chat/completions",
            key_name_of_api_key="DF_API_KEY",
            model_name="gpt-4o",
            max_workers=10
        )

        self.evaluator = BenchDatasetEvaluatorQuestion(
            compare_method="semantic",
            llm_serving=self.llm_serving,
            prompt_template=None,
            support_subquestions=False
        )

    def forward(self):
        self.evaluator.run(
            storage=self.storage.step(),
            input_question_key="question",
            input_test_answer_key="predicted_answer",
            input_gt_answer_key="ground_truth"
        )

if __name__ == "__main__":
    pipeline = MyPipeline()
    pipeline.forward()

4. Key Differences from BenchDatasetEvaluator

1. Question context: Semantic mode includes question field in the prompt alongside answer and reference_answer.
2. Subquestions support: When support_subquestions=True, evaluates multiple subquestions per row.
3. Prompt templates: Uses AnswerJudgePromptQuestion (single question) or AnswerJudgeMultipleQuestionsPrompt (subquestions).

5. AnswerJudgePromptQuestion

AnswerJudgePromptQuestion is the default prompt template class for semantic mode.

Important Notes on prompt_template

Although the source code sets the default value to AnswerJudgePromptQuestion, this default is a class object, not an instance.

In normal usage, it's recommended to use one of these two approaches:

Option 1: Pass None (recommended)

prompt_template=None

This uses the built-in fallback logic.

Option 2: Pass an instance

from dataflow.prompts.core_text import AnswerJudgePromptQuestion

prompt_template=AnswerJudgePromptQuestion()

Fields Passed to build_prompt(...)

When prompt_template is an AnswerJudgePromptQuestion instance, the source code passes these fields:

• question: The question being evaluated
• answer: The predicted answer to evaluate
• reference_answer: The ground truth answer

Expected LLM Response Format

LLM response must contain:

{
  "judgement_result": true  // or false
}