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# FLD
### Paper
Title: Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic
Abstract: https://arxiv.org/abs/2308.07336
**FLD** (**F**ormal **L**ogic **D**eduction) is a deductive reasoning benchmark.
Given a set of facts and a hypothesis, an LLM is required to generate (i) proof steps to (dis-)prove the hypothesis, and (ii) an answer ("proved", "disproved" or unknown").
Unique features of FLD are:
* It assesses the model's logical reasoning ability *isolated from knowledge*, as the facts are randomly constructed so that referring to existing knowledge never helps solve the task.
* It assesses diverse reasoning patterns (i.e., deduction rules), as it is based on formal logic theory.
* As a result, it is highly challenging. Indeed, even GPT-4 can solve only about half of the problems.
Homepage: https://github.com/hitachi-nlp/FLD
### Citation
```
@InProceedings{pmlr-v202-morishita23a,
title = {Learning Deductive Reasoning from Synthetic Corpus based on Formal Logic},
author = {Morishita, Terufumi and Morio, Gaku and Yamaguchi, Atsuki and Sogawa, Yasuhiro},
booktitle = {Proceedings of the 40th International Conference on Machine Learning},
pages = {25254--25274},
year = {2023},
editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan},
volume = {202},
series = {Proceedings of Machine Learning Research},
month = {23--29 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v202/morishita23a/morishita23a.pdf},
url = {https://proceedings.mlr.press/v202/morishita23a.html},
}
```
### Groups and Tasks
#### Groups
* `fld`
#### Tasks
This release is the simplified version of FLD where a model is required to predict only an answer.
This setting is described by "answer accuracy" in the original paper.
* `fld_default` is a basic task based on [FLD.v2](https://huggingface.co/datasets/hitachi-nlp/FLD.v2/viewer/star)
* `fld_star`: is a more challenging version based on [FLD.v2-star](https://huggingface.co/datasets/hitachi-nlp/FLD.v2/viewer/star)
### Checklist
For adding novel benchmarks/datasets to the library:
* [x] Is the task an existing benchmark in the literature?
* [x] Have you referenced the original paper that introduced the task?
* [x] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
* [ ] Is the "Main" variant of this task clearly denoted?
* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?
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