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- ---
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- license: mit
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+ # JustLogic
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+ [[Paper]](https://arxiv.org/abs/2501.14851) [[Github]](https://github.com/michaelchen-lab/JustLogic)
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+ JustLogic is a deductive reasoning datataset that is
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+ 1. highly complex, capable of generating a diverse range of linguistic patterns, vocabulary, and argument structures;
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+ 2. prior knowledge independent, eliminating the advantage of models possessing prior knowledge and ensuring that only deductive reasoning is used to answer questions; and
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+ 3. capable of in-depth error analysis on the heterogeneous effects of reasoning depth and argument form on model accuracy.
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+
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+ ## Dataset Format
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+ - `premises`: List of premises in the question, in the form of a Python list.
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+ - `paragraph`: A paragraph consisting of the above `premises`. This is given as input to models.
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+ - `conclusion`: The expected conclusion of the given premises.
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+ - `question`: The statement in which models must determine its truth-value.
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+ - `label`: True | False | Uncertain
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+ - `arg`: The argument structure
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+ - `statements`: Matching symbols in `arg` to their corresponding natural language statements.
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+ - `depth`: The argument depth of the given question
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+
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+ ## Dataset Construction
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+ JustLogic is a synthetically generated dataset. The script to construct your own dataset can be found in the [Github repo](https://github.com/michaelchen-lab/JustLogic).
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+ ## Citation
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+ ```
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+ @article{chen2025justlogic,
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+ title={JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models},
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+ author={Chen, Michael K and Zhang, Xikun and Tao, Dacheng},
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+ journal={arXiv preprint arXiv:2501.14851},
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+ year={2025}
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+ }
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+ ```
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+
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+ ---
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+ license: mit
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+ ---