Adaptable Logical Control for Large Language Models
<|reference_start|>Adaptable Logical Control for Large Language Models: Despite the success of Large Language Models (LLMs) on various tasks following human instructions, controlling model generation at inference time poses a persistent challenge. In this paper, we introduce Ctrl-G, an adaptable framework that facilitates tractable and flexible control of LLM generation to reliably follow logical constraints. Ctrl-G combines any production-ready LLM with a Hidden Markov Model, enabling LLM outputs to adhere to logical constraints represented as deterministic finite automata. We show that Ctrl-G, when applied to a TULU2-7B model, outperforms GPT3.5 and GPT4 on the task of interactive text editing: specifically, for the task of generating text insertions/continuations following logical constraints, Ctrl-G achieves over 30% higher satisfaction rate in human evaluation compared to GPT4. When applied to medium-size language models (e.g., GPT2-large), Ctrl-G also beats its counterparts for constrained generation by large margins on standard benchmarks. Additionally, as a proof-of-concept study, we experiment Ctrl-G on the Grade School Math benchmark to assist LLM reasoning, foreshadowing the application of Ctrl-G, as well as other constrained generation approaches, beyond traditional language generation tasks.<|reference_end|>
@article{zhang2024adaptable,
    title={Adaptable Logical Control for Large Language Models},
    author={Honghua Zhang, Po-Nien Kung, Masahiro Yoshida, Guy Van den Broeck,
  Nanyun Peng},
    journal={arXiv preprint arXiv:2406.13892},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13892},
    primaryClass={cs.CL}
}