提出分而治之策略，显著提升大模型按词约束生成的能力。
Control Large Language Models via Divide and Conquer
- 将复杂约束分解为子任务，分步控制生成过程。
- 在最难的约束任务中成功率提升超90%。
- 适合需要精准文本控制的应用场景。
本文研究基于提示的大语言模型（LLM）可控生成，聚焦词汇约束生成（LCG）。系统评估了LLM在提示控制下满足词汇约束的表现及其在下游任务中的有效性。结果表明，LLM在提示控制下难以一致满足词汇约束。我们识别出三大限制：(1) 位置偏见，即模型更倾向于满足输入特定位置的约束；(2) 对解码参数响应低，控制效果微弱；(3) 难以处理某些复杂约束（如复合词）。为此，我们提出分而治之生成策略，适用于白盒与黑盒LLM，显著提升LCG性能，在最困难的任务中成功率提升超过90%。分析揭示了提示控制下LLM在LCG中的表现瓶颈，所提策略为更精细、定制化的文本生成提供了路径。
原文摘要 · Abstract (English)
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.
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