arXiv cs.AI· Jiaheng Zhang, Daqiang Zhang·· 1 天前AI 评分8
Prism：解耦式生成推荐解释框架，用知识蒸馏让 140M 学生模型超越 11B 教师
The Oracle and The Prism: A Decoupled and Efficient Framework for Generative Recommendation Explanation
AI 导读
研究者提出解耦框架 Prism，将推荐拆分为排序与解释生成两个独立阶段，并以 FLAN-T5-XXL 作为 Oracle 教师、经知识蒸馏训练 140M 参数的 BART-Base 学生模型生成个性化解释。人类评估中该 140M 模型在忠实度与个性化上显著优于 11B 教师，并展现出纠正教师模型幻觉的能力，同时实现 24 倍加速和 10 倍内存降低。该论文已被作者撤回。
正文 · 原文
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Abstract:The integration of Large Language Models (LLMs) into explainable recommendation systems often leads to a performance-efficiency trade-off in end-to-end architectures, where joint optimization of ranking and explanation can result in suboptimal compromises. To resolve this, we propose Prism, a novel decoupled framework that rigorously separates the recommendation process into a dedicated ranking stage and an explanation generation stage. This decomposition ensures that each component is optimized for its specific objective, eliminating inherent conflicts in coupled models.
Inspired by knowledge distillation, Prism leverages a powerful, instruction-following teacher LLM (FLAN-T5-XXL) as an Oracle to produce high-fidelity explanatory knowledge. A compact, fine-tuned student model (BART-Base), the Prism, then specializes in synthesizing this knowledge into personalized explanations. Our extensive experiments on benchmark datasets reveal a key finding: the distillation process not only transfers knowledge but also acts as a noise filter. Our 140M-parameter Prism model significantly outperforms its 11B-parameter teacher in human evaluations of faithfulness and personalization, demonstrating an emergent ability to correct hallucinations present in the teacher's outputs. While achieving a 24x speedup and a 10x reduction in memory consumption, our analysis validates that decoupling, coupled with targeted distillation, provides an efficient and effective pathway to high-quality, and perhaps more importantly, trustworthy explainable recommendation.
| Comments: | We have identified several issues in the paper |
| Subjects: | Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2511.16543 [cs.IR] |
| (or arXiv:2511.16543v3 [cs.IR] for this version) | |
| https://doi.org/10.48550/arXiv.2511.16543 arXiv-issued DOI via DataCite |
Submission history
From: Jiaheng Zhang [view email]
[v1]
Thu, 20 Nov 2025 16:59:16 UTC (2,231 KB)
[v2]
Sat, 6 Dec 2025 08:27:39 UTC (2,232 KB)
[v3]
Wed, 11 Nov 2026 13:41:40 UTC (1 KB) (withdrawn)
来源：arXiv cs.AI · arxiv.org