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