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update README.md

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@@ -74,9 +74,9 @@ The training split of `nitibench-ccl` can be found in the [WangchanX-Legal-ThaiC
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  Each data point contains four columns:
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  - `question: str` β€” A question relevant to the `relevant_laws`.
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- - `answer_full: str` β€” The original answer generated by an LLM, which has been revised and edited by legal experts to include both the reasoning steps and the final answer.
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  - `relevant_laws: List[Dict[str, str]]` β€” A list of relevant law name, section, and contents.
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- - `answer: str` β€” The answer to the question based on the `relevant_laws`, provided without the reasoning steps.
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  - `reference_laws: List[Dict[str, str]]` - A list of referenced law mentioned in `relevant_laws` column.
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  Formally, given the data triple \((q, T=\{p_1, p_2, \dots, p_K\}, y)\), \(q\) represents the `question`, \(T\) represents `relevant_laws`, and \(y\) represents the `answer`.
 
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  Each data point contains four columns:
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  - `question: str` β€” A question relevant to the `relevant_laws`.
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+ - `answer: str` β€” The original answer generated by an LLM, which has been revised and edited by legal experts to include both the reasoning steps and the final answer.
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  - `relevant_laws: List[Dict[str, str]]` β€” A list of relevant law name, section, and contents.
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+ - `reference_answer: str` β€” The answer to the question based on the `relevant_laws`, provided without the reasoning steps.
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  - `reference_laws: List[Dict[str, str]]` - A list of referenced law mentioned in `relevant_laws` column.
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  Formally, given the data triple \((q, T=\{p_1, p_2, \dots, p_K\}, y)\), \(q\) represents the `question`, \(T\) represents `relevant_laws`, and \(y\) represents the `answer`.