Harnessing the Power of Large Language Models for Natural Language to
  First-Order Logic Translation
Yuan Yang Siheng Xiong Ali Payani Ehsan Shareghi Faramarz Fekri
Translating natural language sentences to first-order logic (NL-FOL
translation) is a longstanding challenge in the NLP and formal logic
literature. This paper introduces LogicLLaMA, a LLaMA-7B model fine-tuned for
NL-FOL translation using LoRA on a single GPU. LogicLLaMA is capable of
directly translating natural language into FOL rules, which outperforms
GPT-3.5. LogicLLaMA is also equipped to correct FOL rules predicted by GPT-3.5,
and can achieve similar performance as GPT-4 with a fraction of the cost. This
correction ability was achieved by a novel supervised fine-tuning (SFT) +
reinforcement learning with human feedback (RLHF) framework, which initially
trains on synthetically perturbed NL-FOL pairs to encourage chain-of-thought
reasoning and then fine-tunes with RLHF on GPT-3.5 outputs using a FOL verifier
as the reward model.
  To train LogicLLaMA, we present MALLS (large language $\textbf{M}$odel
gener$\textbf{A}$ted N$\textbf{L}$-FO$\textbf{L}$ pair$\textbf{S}$), a dataset
of 34K high-quality and diverse sentence-level NL-FOL pairs collected from
GPT-4. The dataset was created by implementing a pipeline that prompts GPT-4
for pairs, and dynamically adjusts the prompts to ensure the collection of
pairs with rich and diverse contexts at different levels of complexity, and
verifies the validity of the generated FOL rules. Codes, weights, and data are
available at $\href{https://github.com/gblackout/LogicLLaMA}{{\small
\text{https://github.com/gblackout/LogicLLaMA}}}$.