3 papers
cs.CL2024
REInstruct: Building Instruction Data from Unlabeled Corpus
Shu Chen, Xinyan Guan, Yaojie Lu +3
Manually annotating instruction data for large language models is difficult, costly, and hard to scale. Meanwhile, current automatic annotation methods typically rely on distilling…
cs.CL2024
Executing Natural Language-Described Algorithms with Large Language Models: An Investigation
Xin Zheng, Qiming Zhu, Hongyu Lin +3
Executing computer programs described in natural language has long been a pursuit of computer science. With the advent of enhanced natural language understanding capabilities exhib…
cs.CL2023
Toward Unified Controllable Text Generation via Regular Expression Instruction
Xin Zheng, Hongyu Lin, Xianpei Han +1
Controllable text generation is a fundamental aspect of natural language generation, with numerous methods proposed for different constraint types. However, these approaches often…