---
license: mit
task_categories:
- question-answering
- text-classification
language:
- en
arxiv: 2501.14851
---
# JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models

[[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
---