README.md

---
license: apache-2.0
language:
- en
---

# LogicLLaMA Model Card

## Model details

LogicLLaMA is a language model that translates natural-language (NL) statements into first-order logic (FOL) rules.
It is trained by fine-tuning the LLaMA-7B model on the [MALLS](https://huggingface.co/datasets/yuan-yang/MALLS-v0) dataset.

**Model type:**
This repo contains the LoRA delta weights for naive correction LogicLLaMA, which, given a pair of the NL statement and a predicted FOL rule, 
corrects the potential errors in the predicted FOL rule.
This is used as a downstream model together with ChatGPT, where ChatGPT does the "heavy lifting" by predicting the initial translated FOL rule 
and then LogicLLaMA refines the rule by correcting potential errors.
In our [experiments](https://arxiv.org/abs/2305.15541), this mode yields better performance than ChatGPT and direction translation LogicLLaMA.

We also provide the delta weights for other modes:
- [direct translation LogicLLaMA ](https://huggingface.co/yuan-yang/LogicLLaMA-7b-direct-translate-delta-v0)

**License:**
Apache License 2.0

## Using the model

Check out how to use the model on our project page:  https://github.com/gblackout/LogicLLaMA


**Primary intended uses:**
LogicLLaMA is intended to be used for research.


## Citation

```
@article{yang2023harnessing,
      title={Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation}, 
      author={Yuan Yang and Siheng Xiong and Ali Payani and Ehsan Shareghi and Faramarz Fekri},
      journal={arXiv preprint arXiv:2305.15541},
      year={2023}
}
```