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--- |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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# LogicLLaMA Model Card |
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## Model details |
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LogicLLaMA is a language model that translates natural-language (NL) statements into first-order logic (FOL) rules. |
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It is trained by fine-tuning the LLaMA-7B model on the [MALLS](https://huggingface.co/datasets/yuan-yang/MALLS-v0) dataset. |
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**Model type:** |
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This repo contains the LoRA delta weights for naive correction LogicLLaMA, which, given a pair of the NL statement and a predicted FOL rule, |
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corrects the potential errors in the predicted FOL rule. |
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This is used as a downstream model together with ChatGPT, where ChatGPT does the "heavy lifting" by predicting the initial translated FOL rule |
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and then LogicLLaMA refines the rule by correcting potential errors. |
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In our [experiments](https://arxiv.org/abs/2305.15541), this mode yields better performance than ChatGPT and direction translation LogicLLaMA. |
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We also provide the delta weights for other modes: |
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- [direct translation LogicLLaMA ](https://huggingface.co/yuan-yang/LogicLLaMA-7b-direct-translate-delta-v0) |
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**License:** |
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Apache License 2.0 |
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## Using the model |
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Check out how to use the model on our project page: https://github.com/gblackout/LogicLLaMA |
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**Primary intended uses:** |
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LogicLLaMA is intended to be used for research. |
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## Citation |
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``` |
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@article{yang2023harnessing, |
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title={Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation}, |
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author={Yuan Yang and Siheng Xiong and Ali Payani and Ehsan Shareghi and Faramarz Fekri}, |
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journal={arXiv preprint arXiv:2305.15541}, |
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year={2023} |
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} |
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``` |
