NL2TL: Transforming natural languages to temporal logics using large language models
New research fromReliable Autonomous Systems Lab at MIT (REALM) proposes an accurate and generalizable transformation framework of English instructions from natural language to temporal logic, exploring the use of large language models (LLMs) at multiple stages.
Authors: Yongchao Chen (first author), Rujul Gandhi, Yang Zhang, Chuchu Fan (corresponding author)
Citation: 2023 Conference on Empirical Methods on Natural Language Processing (EMNLP’2023)
Project webpage: https://yongchao98.github.io/MIT-realm-NL2TL/
Demo website: http://realm-02.mit.edu:8444
Abstract
Temporal Logic (TL) can be used to rigorously specify complex high-level specification for systems in many engineering applications. The translation between natural language (NL) and TL has been under-explored due to the lack of dataset and generalizable model across different application domains. In this paper, we propose an accurate and generalizable transformation framework of English instructions from NL to TL, exploring the use of Large Language Models (LLMs) at multiple stages. Our contributions are twofold. First, we develop a framework to create a dataset of NL-TL pairs combining LLMs and human annotation. We publish a dataset with 28K NL-TL pairs. Then, we finetune T5 models on the lifted versions (i.e., the specific Atomic Propositions (AP) are hidden) of the NL and TL. The enhanced generalizability originates from two aspects: 1) Usage of lifted NL-TL characterizes common logical structures, without constraints of specific domains. 2) Application of LLMs in dataset creation largely enhances corpus richness. We test the generalization of trained models on five varied domains. To achieve full NL-TL transformation, we either combine the lifted model with AP recognition task or do the further finetuning on each specific domain. During the further finetuning, our model achieves higher accuracy (>95%) using only <10% training data, compared with the baseline sequence to sequence (Seq2Seq) model.
