A Token-Agnostic Approach to Controlling Generated Text Length in Large Language Models

The rapid expansion of language models has led to increased demand for precise control over text generation, particularly in terms of output length. Traditional token-based methods often struggle with consistency across languages and text coherence, presenting challenges in tasks that require strict length adherence. A novel token-agnostic approach has been developed to address these limitations, leveraging semantic structures such as sentences and paragraphs to manage length dynamically. Through this method, text generation becomes more flexible and adaptable to a variety of languages and writing styles, ensuring that length constraints are respected without sacrificing fluency or relevance. Experimental results demonstrate the effectiveness of the method when implemented with Llama, yielding high precision in length adherence and text quality across multiple evaluation metrics. This approach offers a robust solution to the ongoing challenge of managing output length in text generation, with potential applications spanning numerous domains, from summarization to content creation.