Controllable Text Generation in the Instruction-Tuning Era

arXiv:2405.01490v1 Announce Type: new
Abstract: While most research on controllable text generation has focused on steering base Language Models, the emerging instruction-tuning and prompting paradigm offers an alternate approach to controllability. We compile and release ConGenBench, a testbed of 17 different controllable generation tasks, using a subset of it to benchmark the performance of 9 different baselines and methods on Instruction-tuned Language Models. To our surprise, we find that prompting-based approaches outperform controllable text generation methods on most datasets and …

abstract arxiv benchmark cs.ai cs.cl language language models paradigm performance prompting release research tasks text text generation type while