arxiv preprint – Language Models can be Logical Solvers

In this episode we discuss Language Models can be Logical Solvers
by Jiazhan Feng, Ruochen Xu, Junheng Hao, Hiteshi Sharma, Yelong Shen, Dongyan Zhao, Weizhu Chen. The paper presents LOGIPT, a new language model designed to tackle complex logical reasoning by directly mimicking the reasoning process of logical solvers, which avoids errors caused by parsing natural language into symbolic representations. LOGIPT is fine-tuned using a dataset that captures the hidden reasoning steps of deductive solvers, ensuring strict adherence to solver syntax and grammar. The model’s performance surpasses that of existing solver-augmented language models and few-shot prompting techniques on benchmark deductive reasoning datasets.