NeurIPS 2025 | Language Ranker：从推荐系统的视角反思并优化大模型解码过程

Core Insights - The article presents a new perspective on large language models (LLMs) by comparing their decoding process to the ranking stage of recommendation systems, highlighting the limitations of existing decoding methods and proposing an efficient, lightweight improvement framework called Language Ranker [2][3][33]. Group 1: Understanding LLMs - LLMs can be viewed as a specialized recommendation system that selects the most suitable responses from a vast candidate response space based on user input [3]. - The key components of LLMs correspond to those in recommendation systems, allowing for a clearer understanding of the limitations of current methods [6][11]. Group 2: Language Ranker Framework - Language Ranker framework is designed to overcome the limitations of traditional reward models by reusing features extracted from the main model, thus requiring only a small learning module for candidate response re-ranking [8][9]. - The framework consists of three steps: candidate recall, feature extraction, and candidate ranking, which collectively enhance the decoding process [10][14]. Group 3: Experimental Results - Language Ranker, with less than 0.5 million parameters, achieves performance comparable to large-scale reward models across various tasks, demonstrating significant efficiency [19][20]. - In the MBPP task, Language Ranker can be trained in just 67 seconds on a CPU, while traditional reward models take over an hour [21][23]. - The framework exhibits strong cross-task and cross-model adaptability, allowing a single Ranker to work across different tasks, thus reducing model management costs [24][26]. Group 4: Future Outlook - Language Ranker represents a new paradigm for optimizing the decoding phase of LLMs, emphasizing the importance of efficient selection of optimal answers rather than merely increasing model size [33]. - The framework supports personalized extensions, enabling the same main model to be paired with different Rankers to meet diverse application needs [15][33].