Textual Aesthetics in Large Language Models

Image aesthetics is a crucial metric in the field of image generation. However, textual aesthetics has not been sufficiently explored. With the widespread application of large language models (LLMs), previous work has primarily focused on the correctness of content and the helpfulness of responses. Nonetheless, providing responses with textual aesthetics is also an important factor for LLMs, which can offer a cleaner layout and ensure greater consistency and coherence in content. Additionally, we develop two evaluation methods for textual aesthetics based on text and image analysis, respectively. Our experiments demonstrate that using textual aesthetics data and employing the TAPO fine-tuning method not only improves aesthetic scores but also enhances performance on general evaluation datasets such as AlpacalEval and Anera-hard. Image aesthetics (Huang et al., 2024a; Murray et al., 2012; Kong et al., 2016; Ke et al., 2021; Bosse et al., 2017) has emerged as a prominent research area within computer vision, focusing on assessing and improving the visual appeal of images. Aesthetics has recently been integrated into state-ofthe-art image generation models, such as diffusion models (Rombach et al., 2022), significantly enhancing the visual quality of generated images (Wu et al., 2024a; 2023) and aligning them more closely with human preferences (Huang et al., 2024a; Wu et al., 2024b; 2023). Meanwhile, advancements in large language models (LLMs) like ChatGPT (OpenAI, 2023) and LLaMA (Touvron et al., 2023b; Dubey et al., 2024) have demonstrated impressive generative capabilities across various domains, including code, articles, and web content. Although LLMs have made significant progress in generating textual content, enhancing the aesthetic quality of their output remains a critical challenge.