arXiv cs.CL· XinYu Piao, Myunghoon Ryu, Joongheon Kim, Jong-Kook Kim·· 13 小时前AI 评分34
SKIM：让冻结语言模型在参数内自键记忆实现无外部存储的对话召回
Self-Keyed In-Parameter Memorization for External Memory-Free Dialogue Recall in Frozen Language Models
AI 导读
研究者提出 self-keyed in-parameter memorization（SKIM），一种闭式方法：模型为每轮对话自行生成多个问题并写入参数作为检索键，之后仅凭参数即可从用户的不同措辞提问中召回该轮内容。在五个语言模型上评估，SKIM 能从后续用户提问中召回大部分已存储对话轮次，且无需任何外部存储。
正文 · 原文
Abstract:A large language model maintains conversational content in an external storage that is outside the model and grows as the dialogue continues. This work assumes an environment where a language model has no external memory and no prior knowledge of the future question or its answer. The motivation is to research whether a dialogue can be stored in the model parameters and later answer a question about it. This work proposes self-keyed in-parameter memorization (SKIM), a closed-form method in which the model generates its own questions about each dialogue turn and writes those questions into its parameters as the keys that later retrieve the turn's content. Generating many varied questions for each turn allows a later user question to match one even when phrased differently, and its answer is recalled from the parameters alone. Evaluated on five language models, SKIM recalls most stored dialogue turns from later user questions, and it answers from the model parameters without any external storage.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.36948 [cs.CL] |
| (or arXiv:2610.36948v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36948 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: XinYu Piao [view email]
[v1]
Tue, 27 Oct 2026 10:10:11 UTC (1,027 KB)
来源：arXiv cs.CL · arxiv.org