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

Juncheng Xie; Hung-yi Lee

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arXiv · 2508.13805

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

Juncheng Xie·Hung-yi Lee

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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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BibTeX RIS

Juncheng Xie, Hung-yi Lee. 2025-08-19. Prompt-Based One-Shot Exact Length-Controlled Generation with LLMs. https://arxiv.org/abs/2508.13805

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