Controllable Text Generation in the Instruction-Tuning Era

• URL: http://arxiv.org/abs/2405.01490v1
• Date: Thu, 2 May 2024 17:24:30 GMT
• Title: Controllable Text Generation in the Instruction-Tuning Era
• Authors: Dhananjay Ashok, Barnabas Poczos,
• Abstract summary: We find that prompting-based approaches outperform controllable text generation methods on most datasets and tasks. We provide an algorithm that uses only a task dataset and a Large Language Model with in-context capabilities to automatically generate a constraint dataset.
• Score: 3.310278632293704
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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 tasks, highlighting a need for research on controllable text generation with Instruction-tuned Language Models in specific. Prompt-based approaches match human performance on most stylistic tasks while lagging on structural tasks, foregrounding a need to study more varied constraints and more challenging stylistic tasks. To facilitate such research, we provide an algorithm that uses only a task dataset and a Large Language Model with in-context capabilities to automatically generate a constraint dataset. This method eliminates the fields dependence on pre-curated constraint datasets, hence vastly expanding the range of constraints that can be studied in the future.

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