Despite remarkable progress in steganography, embedding sentence-level information into cover media remains challenging. In this work, we present the concept of \textbf{Semantic-Payload Steganography}, which aims to hide semantically meaningful and structured content, such as sentences, in cover media. Based on this concept, we present Sentence-to-Image Steganography as an instance that enables the hiding of sentence-level messages within a cover image. To accomplish this feat, we propose \textbf{S}emantic-Payload \textbf{S}teganographic \textbf{L}anguage \textbf{M}odel ($\mathbf{S^2LM}$), which leverages large language models to embed high-level textual information into images. $\mathbf{S^2LM}$ redesigns the entire pipeline, involving the LLM throughout the process to enable the hiding and recovery of sentences. To evaluate semantic-payload steganography methods, we establish a benchmark named \textbf{I}n\textbf{v}isible \textbf{T}ext (IVT), comprising a diverse set of corpus as secret messages. Experimental results demonstrate that $\mathbf{S^2LM}$ effectively recovers natural-language messages while maintaining high visual quality. The source code will be released at https://github.com/aoli-gei/S2LM.

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