Length-controllable text generation (LCTG) is essential for tasks like text summarization and report generation. However, large language models (LLMs) have limited awareness of output length, so precise control over the length of generated text remains a significant challenge. Most existing methods focus on prompt-based frameworks, position encoding, and reinforcement learning for model training. These approaches may affect semantic quality, and struggle to maintain consistent length control across different models and tasks. In this paper, we propose DcLM, a model-agnostic approach that introduces dynamic length markers to guide length-controllable outputs. During training, the model leverages these markers as in-context information, without learning to generate them. At inference time, an external word counter and injected length information guide the model to produce outputs of accurate lengths. We evaluate our method across multiple datasets, and the experimental results demonstrate that DcLM significantly reduces length deviation, showcasing its robust generalization ability across various length scales and tasks.

Measurement scales play a crucial role in quantifying the nuanced dimensions of human cognition and behavior, however, their development typically demands extensive manual labor, and current methodologies lack systematic automation and standardized evaluation. In this paper, we introduce AutoScale, a pioneering multi-agent framework that automates scale development by leveraging collaborative AI agents. Our contributions are threefold: (1) a novel multi-agent LLM-based framework for end-to-end scale generation that replicates expert collaboration and iterative data-driven refinement, (2) the first comprehensive dataset, SCALE-1.2K, comprising 1.2K validated scales across 16 psychological domains, establishing a benchmark for automated scale development, and (3) a multi-dimensional evaluation system, featuring Muti-LLM-as-judge for conceptual and linguistic assessment and simulated large-scale testing for rigorous psychometric verification. Experimental results demonstrate that AutoScale streamlines the scale development process while maintaining rigorous quality standards, significantly reducing manual effort and paving the way for more efficient and objective measurement design in diverse research fields.