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@@ -79,7 +79,6 @@ The dataset is organized into three separate subsets, each targeting different e
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  - `question`: The question text
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  - `choices`: List of possible answer choices
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  - `answer`: The correct answer
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- - `question_type`: Type/category of the legal question
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  ### 2. Natural Language Inference (`nli_questions`)
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@@ -93,7 +92,6 @@ The dataset is organized into three separate subsets, each targeting different e
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  - `question`: The inference question to be evaluated
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  - `choices`: Possible relationship labels (typically entailment, contradiction, neutral)
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  - `answer`: The correct logical relationship
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- - `question_type`: Category of the inference task
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  ### 3. Syllogism Questions (`syllogism_questions`)
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@@ -104,7 +102,6 @@ The dataset is organized into three separate subsets, each targeting different e
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  **Features**:
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  - `question`: The syllogistic reasoning question
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  - `answer`: Expected reasoning or conclusion
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- - `question_type`: Type of syllogistic reasoning required
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  ## Evaluation Methodology
 
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  - `question`: The question text
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  - `choices`: List of possible answer choices
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  - `answer`: The correct answer
 
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  ### 2. Natural Language Inference (`nli_questions`)
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  - `question`: The inference question to be evaluated
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  - `choices`: Possible relationship labels (typically entailment, contradiction, neutral)
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  - `answer`: The correct logical relationship
 
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  ### 3. Syllogism Questions (`syllogism_questions`)
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  **Features**:
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  - `question`: The syllogistic reasoning question
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  - `answer`: Expected reasoning or conclusion
 
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  ## Evaluation Methodology