Planning with Logical Graph-based Language Model for Instruction Generation
This addresses the challenge of generating logically consistent instructions for applications requiring precise domain knowledge, representing an incremental improvement in specialized text generation.
The paper tackles the problem of generating logically correct instructional texts by proposing Logical-GLM, a graph-based language model that infuses domain knowledge through logical graphs, resulting in more valid text generation with smaller-scale training data and fewer parameters compared to traditional models.
Despite the superior performance of large language models to generate natural language texts, it is hard to generate texts with correct logic according to a given task, due to the difficulties for neural models to capture implied rules from free-form texts. In this paper, we propose a novel graph-based language model, Logical-GLM, to infuse logic into language models for more valid text generation and interpretability. Specifically, we first capture information from natural language instructions and construct logical bayes graphs that generally describe domains. Next, we generate logical skeletons to guide language model training, infusing domain knowledge into language models. Finally, we alternately optimize the searching policy of graphs and language models until convergence. The experimental results show that Logical-GLM is both effective and efficient compared with traditional language models, despite using smaller-scale training data and fewer parameters. Our approach can generate instructional texts with more correct logic owing to the internalized domain knowledge. Moreover, the usage of logical graphs reflects the inner mechanism of the language models, which improves the interpretability of black-box models.