Recently, large language models (LLMs) have advanced recommendation systems (RSs), and recent works have begun to explore how to integrate LLMs into industrial RSs. While most approaches deploy LLMs offline to generate and pre-cache augmented representations for RSs, high-dimensional representations from LLMs introduce substantial storage and computational costs. Thus, it is crucial to compress LLM representations effectively. However, we identify a counterintuitive phenomenon during representation compression: Mid-layer Representation Advantage (MRA), where representations from middle layers of LLMs outperform those from final layers in recommendation tasks. This degraded final layer renders existing compression methods, which typically compress on the final layer, suboptimal. We interpret this based on modularity theory that LLMs develop spontaneous internal functional modularity and force the final layer to specialize in the proxy training task. Thus, we propose \underline{M}odul\underline{a}r \underline{R}epresentation \underline{C}ompression (MARC) to explicitly control the modularity of LLMs. First, Modular Adjustment explicitly introduces compression and task adaptation modules, enabling the LLM to operate strictly as a representation-learning module. Next, to ground each module to its specific task, Modular Task Decoupling uses information constraints and different network structures to decouple tasks. Extensive experiments validate that MARC addresses MRA and produces efficient representations. Notably, MARC achieved a 2.82% eCPM lift in an online A/B test within a large-scale commercial search advertising scenario.

翻译：近期，大型语言模型（LLM）推动了推荐系统（RS）的发展，已有研究开始探索如何将LLM集成到工业级推荐系统中。尽管多数方法采用离线部署LLM的方式生成并预缓存推荐系统的增强表示，但LLM产生的高维表示带来了巨大的存储和计算成本。因此，有效压缩LLM表示至关重要。然而，我们在表示压缩过程中发现一个反直觉现象——中间层表示优势（MRA）：在推荐任务中，LLM中间层的表示性能显著优于最终层。这种退化的最终层导致现有通常压缩最终层的压缩方法效果欠佳。我们基于模块化理论对此进行解释：LLM会自发形成内部功能模块化，并迫使最终层专注于代理训练任务。为此，我们提出模块化表示压缩（MARC）以显式控制LLM的模块化。首先，模块化调整通过引入压缩与任务适配模块，使LLM严格作为表示学习模块运行。其次，为将各模块锚定至特定任务，模块化任务解耦利用信息约束与差异化网络结构实现任务分离。大量实验验证MARC能有效解决MRA问题并产生高效表示。值得注意的是，在大型商业搜索广告场景的在线A/B测试中，MARC实现了2.82%的eCPM提升。