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arXiv Computation and Language By Yijiang River Dong, Zexue He, Cheng Yu, Tiancheng Hu, Michael J Ryan, Jan Batzner, Zheng Hui, Shaolong Wu, Orestis Papakyriakopoulos, Alex Pentland, Nigel Collier, Jiaxin Pei

Memory-Aware Reward Modeling: Learning Human Preferences for LLM Memory Use

Read the original on arXiv Computation and Language →

The paper investigates whether using a language model’s memory to personalize responses aligns with user preferences. By collecting about 1,000 pairwise judgments from 87 participants on ChatGPT queries that included personal memories, the authors find that memory‑conditioned responses are not consistently preferred and that users sometimes strongly favor non‑memory responses. An automatic reward model trained on these judgments achieves 72.8% preference prediction accuracy and outperforms existing baselines, highlighting the need for evaluation and optimization objectives that better reflect user preferences for memory use.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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