Control Large Language Models via Divide and Conquer
<|reference_start|>Control Large Language Models via Divide and Conquer: This paper investigates controllable generation for large language models (LLMs) with prompt-based control, focusing on Lexically Constrained Generation (LCG). We systematically evaluate the performance of LLMs on satisfying lexical constraints with prompt-based control, as well as their efficacy in downstream applications. We conclude that LLMs face significant challenges in consistently satisfying lexical constraints with prompt-based control. We identified three key limitations of LLMs for LCG, including (1) position bias, where LLMs tend to satisfy constraints that appear in specific positions within the input; (2) low responsiveness to decoding parameters, which render minimal impact on control of LLMs; and (3) struggle with handling the inherent complexity of certain constraints (e.g., compound words). To address these issues, we introduce a Divide and Conquer Generation strategy, effective for both white-box and black-box LLMs, to enhance LLMs performance in LCG tasks, which demonstrates over 90% improvement on success rate in the most challenging LCG task. Our analysis provides valuable insights into the performance of LLMs in LCG with prompt-based control, and our proposed strategy offers a pathway to more sophisticated and customized text generation applications.<|reference_end|>
@article{li2024control,
    title={Control Large Language Models via Divide and Conquer},
    author={Bingxuan Li, Yiwei Wang, Tao Meng, Kai-Wei Chang, Nanyun Peng},
    journal={arXiv preprint arXiv:2410.04628},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04628},
    primaryClass={cs.CL}
}