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DECIDER: A Dual-System Rule-Controllable Decoding Framework for Language Generation (2403.01954v3)

Published 4 Mar 2024 in cs.CL, cs.AI, and cs.LO

Abstract: Constrained decoding approaches aim to control the meaning or style of text generated by a Pre-trained LLM (PLM) using specific target words during inference. However, these methods often guide plausible continuations by greedily selecting targets, which, while completing the task, may disrupt the natural patterns of human language generation. In this work, we propose a novel decoding framework, DECIDER, which enables us to program rules on how we complete tasks to control a PLM. Differing from previous work, our framework transforms the encouragement of target words into the encouragement of all words that satisfy the rule. Specifically, DECIDER is a dual system where a PLM is equipped with a First-OrderLogic (FOL) reasoner to express and evaluate the rules, and a decision function to merge the outputs from both systems to steer the generation. Experiments on CommonGen and PersonaChat demonstrate that DECIDER can effectively follow given rules to achieve generation tasks in a more human-like manner.

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Authors (11)
  1. Chen Xu (186 papers)
  2. Tian Lan (162 papers)
  3. Changlong Yu (22 papers)
  4. Wei Wang (1793 papers)
  5. Jun Gao (267 papers)
  6. Yu Ji (28 papers)
  7. Qunxi Dong (2 papers)
  8. Kun Qian (87 papers)
  9. Piji Li (75 papers)
  10. Wei Bi (62 papers)
  11. Bin Hu (217 papers)
Citations (1)
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