用信息论选用户记忆，让大模型回复更准更省算力
Response-Aware User Memory Selection for LLM Personalization
- 基于响应不确定性优化选择记忆项，不只看语义相似度
- 相比现有方法，生成质量提升且计算成本降低95%
- 适合需要高效个性化的大模型应用
大型语言模型个性化常通过在推理时将部分用户记忆融入提示来实现。现有方法主要依赖记忆项与输入查询的语义相似度进行选择，忽略了其对模型输出分布的实际影响。本文提出响应效用优化的记忆选择方法（RUMS），通过测量记忆子集与模型输出之间的互信息，筛选能降低响应不确定性和增强预测精度的记忆项。实验表明，该信息论基础的方法更贴近人类选择行为，使模型规模扩大400倍；同时，使用RUMS选择的记忆项可显著提升生成质量，计算成本最高降低95%。
原文摘要 · Abstract (English)
A common approach to personalization in large language models (LLMs) is to incorporate a subset of the user memory into the prompt at inference time to guide the model's generation. Existing methods select these subsets primarily using similarity between user memory items and input queries, ignoring how features actually affect the model's response distribution. We propose Response-Utility optimization for Memory Selection (RUMS), a novel method that selects user memory items by measuring the mutual information between a subset of memory and the model's outputs, identifying items that reduce response uncertainty and sharpen predictions beyond semantic similarity. We demonstrate that this information-theoretic foundation enables more principled user memory selection that aligns more closely with human selection compared to state-of-the-art methods, and models $400\times$ larger. Additionally, we show that memory items selected using RUMS result in better response quality compared to existing approaches, while having up to $95\%$ reduction in computational cost.
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