DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation

Understand

Constrained decoding approaches aim to control the meaning or style of text generated by the pre-trained large language models (LLMs or also PLMs) for various tasks at inference time.

• However, these methods often guide plausible continuations by greedily and explicitly selecting targets.
• Though fulfilling the task requirements, these methods may overlook certain general and natural logics that humans would implicitly follow towards such targets.
• Inspired by cognitive dual-process theory, in this work, we propose a novel decoding framework DECIDER where the base LLMs are equipped with a First-Order Logic (FOL) reasoner to express and evaluate the rules, along with a decision function that merges the outputs of both systems to guide the generation.