Feat/open cultural#74
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this file is not needed in the PR
matyasosvath
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Jun 24, 2026
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| def get_target_text(entry: Dict[str, Any]) -> Any: | ||
| if "gold_answer" in entry: | ||
| return entry["gold_answer"] | ||
| return None |
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this fn can be replaced with: entry.get("gold_answer")
>>> d = {"a": 0}
>>> d.get("b")
>>> d.get("b") is None
True
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| if answer_type == "entity": | ||
| return normalize_text(text, remove_punctuation=True) | ||
| if answer_type == "short_answer": | ||
| return normalize_text(text, remove_punctuation=True) |
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you can check these in one if condition
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Summary
This PR introduces a new open-ended cultural evaluation task to HugMe.
Key Features
Open-Ended Question Format
The questions use 3 answer types:
entity- short factual answersshort_answer- brief sentence-length responsesexplanation- longer, more detailed answersEvaluation Methodology
The evaluation uses a hybrid scoring approach depending on the answer type.
Entity answers
For
entityquestions, the dataset provides a list of accepted aliases. Evaluation is performed using:Exact string matching
Alias matching
Substring checks against accepted answers
This enables robust evaluation of short factual responses while remaining deterministic.
Short Answer & Explanation Answers
For
short_answersandexplanationtype answers it uses an LLM-as-judge to evaluate the responses.The judge assigns one of four verdicts according to a task-specific rubric:
Scoring and Reporting
It saves the score categorically and a commulative score like in other tasks, but it also saves a stat of the verdict cases:
Handling Uncertain Cases
Responses that receive an Uncertain verdict are automatically exported to a separate JSON file. This enables:
Result file modification
Furthermore the eval-result files in case of the cultural task now contain the
question_idso later the multiple choice and open ended results can be cross evaluated.