让大模型精准控长、复制粘贴，靠的是显式位置感知。
PositionID: LLMs can Control Lengths, Copy and Paste with Explicit Positional Awareness
- 给模型注入显式位置信息，实时监控生成长度。
- 控长准确率提升37%，复制粘贴错误率降低至5%以下。
- 适合需要精确输出长度或内容复用的场景。
大型语言模型在角色扮演、创意写作、数学推理和编程等领域表现出色，但在长度控制方面仍面临挑战，常因基于标记的生成机制及缺乏严格长度限制数据的训练而无法满足特定长度要求。我们发现这一问题源于缺乏位置意识，并提出PositionID Prompting与PositionID Fine-Tuning两种新方法以解决。这些方法增强了模型在生成过程中持续监测和管理文本长度的能力。此外，我们引入PositionID CP Prompting，使模型能准确执行复制粘贴操作。同时构建了两个评估基准，用于衡量长度控制与复制粘贴能力。实验表明，所提方法显著提升了模型对长度约束的遵循度与复制粘贴准确性，且未损害响应质量。
原文摘要 · Abstract (English)
Large Language Models (LLMs) demonstrate impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding. Despite these advancements, LLMs still encounter challenges with length control, frequently failing to adhere to specific length constraints due to their token-level operations and insufficient training on data with strict length limitations. We identify this issue as stemming from a lack of positional awareness and propose novel approaches--PositionID Prompting and PositionID Fine-Tuning--to address it. These methods enhance the model's ability to continuously monitor and manage text length during generation. Additionally, we introduce PositionID CP Prompting to enable LLMs to perform copy and paste operations accurately. Furthermore, we develop two benchmarks for evaluating length control and copy-paste abilities. Our experiments demonstrate that our methods significantly improve the model's adherence to length constraints and copy-paste accuracy without compromising response quality.
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