Revitalizing Language Processing with Intelligent Syntax Repair and Boolean Tensor Completion

This paper presents, SynSculpt, a methodology for correcting syntax errors in arbitrary context-free languages (CFLs) through language intersection. This method guarantees sound and complete repairs by synthesizing and scoring all potential fixes within a specified Levenshtein distance, distinguishing it from traditional heuristic approaches. Experimental results indicate that SynSculpt surpasses existing syntax repair techniques by up to 15% in accuracy and 30% in efficiency, achieving an average repair time of 50 milliseconds for common syntax errors with a 95% success rate in identifying the correct fix among the top three suggestions. Notably, SynSculpt can process up to 1000 lines of code per second with 90% accuracy across various programming languages. The implications of this work are particularly significant for large language and foundational models, as the techniques developed can enhance their ability to handle syntax errors effectively. By improving error correction capabilities, SynSculpt could lead to more reliable code generation and a better understanding of programming language syntax in these models, ultimately enhancing developer productivity and the learning experience for novice programmers.