Prompt-Based One-Shot Exact Length-Controlled Generation with LLMs

• URL: http://arxiv.org/abs/2508.13805v1
• Date: Tue, 19 Aug 2025 13:12:01 GMT
• Title: Prompt-Based One-Shot Exact Length-Controlled Generation with LLMs
• Authors: Juncheng Xie, Hung-yi Lee,
• Abstract summary: We present a prompt-based strategy that compels an off-the-shelf large language model to generate exactly a desired number of tokens.<n>The prompt appends countdown markers and explicit counting rules so that the model "writes while counting"<n>On MT-Bench-LI, strict length compliance with GPT-4.1 leaps from below 30% under naive prompts to above 95% with our countdown prompt.
• Score: 56.47577824219207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Controlling the length of text produced by large language models (LLMs) remains challenging: models frequently overshoot or undershoot explicit length instructions because they cannot reliably keep an internal token count. We present a prompt-based, one-shot strategy that compels an off-the-shelf LLM to generate exactly a desired number of tokens - words (English) or characters (Chinese) - without any fine-tuning or iterative sampling. The prompt appends countdown markers and explicit counting rules so that the model "writes while counting." We evaluate on four settings: open-ended generation (1-1000 tokens), XSUM summarization, MT-Bench-LI instruction following, and the LIFEBENCH equal-length track. On MT-Bench-LI, strict length compliance with GPT-4.1 leaps from below 30% under naive prompts to above 95% with our countdown prompt, surpassing the popular draft-then-revise baseline, while judged answer quality is preserved. These results show that precise length control can be achieved through prompt engineering alone, offering a lightweight alternative to training- or decoding-based methods.

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