| # 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? | |