Prompt-Based One-Shot Exact Length-Controlled Generation with LLMs
This provides a lightweight solution for applications requiring exact text length control, though it is incremental as it builds on existing prompt engineering techniques.
The paper tackles the problem of controlling text length in large language models, presenting a prompt-based one-shot strategy that achieves over 95% strict length compliance with GPT-4.1 on MT-Bench-LI, up from below 30% with naive prompts.
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.