Model scaling is becoming the default choice for many language tasks due to the success of large language models (LLMs). However, it can fall short in specific scenarios where simple customized methods excel. In this paper, we delve into the patent approval pre-diction task and unveil that simple domain-specific graph methods outperform enlarging the model, using the intrinsic dependencies within the patent data. Specifically, we first extend the embedding-based state-of-the-art (SOTA) by scaling up its backbone model with various sizes of open-source LLMs, then explore prompt-based methods to harness proprietary LLMs' potential, but find the best results close to random guessing, underlining the ineffectiveness of model scaling-up. Hence, we propose a novel Fine-grained cLAim depeNdency (FLAN) Graph through meticulous patent data analyses, capturing the inherent dependencies across segments of the patent text. As it is model-agnostic, we apply cost-effective graph models to our FLAN Graph to obtain representations for approval prediction. Extensive experiments and detailed analyses prove that incorporating FLAN Graph via various graph models consistently outperforms all LLM baselines significantly. We hope that our observations and analyses in this paper can bring more attention to this challenging task and prompt further research into the limitations of LLMs. Our source code and dataset can be obtained from http://github.com/ShangDataLab/FLAN-Graph.

翻译：模型扩展因大型语言模型（LLMs）的成功而成为许多语言任务的默认选择。然而，在简单定制化方法更具优势的特定场景中，模型扩展可能效果不佳。本文深入探究专利审批预测任务，发现利用专利数据内在依赖关系的简单领域特定图方法优于模型规模扩展。具体而言，我们首先通过使用不同规模的开源LLMs扩展骨干模型，对基于嵌入的最先进方法（SOTA）进行优化，进而探索基于提示的方法以发挥专有LLMs的潜力，但发现最优结果接近随机猜测，凸显了模型扩展的无效性。因此，我们通过对专利数据进行细致分析，提出了一种新颖的细粒度权利要求依赖图（FLAN Graph），捕获专利文本段落间的内在依赖关系。由于该图是模型无关的，我们将成本效益高的图模型应用于FLAN Graph，以获得用于审批预测的表示。大量实验和详细分析证明，通过多种图模型引入FLAN Graph的效果始终显著优于所有LLMs基线方法。我们希望本文的观察与分析能引起对这一挑战性任务的更多关注，并推动针对LLMs局限性的进一步研究。我们的源代码和数据集可从http://github.com/ShangDataLab/FLAN-Graph获取。