Rethinking Memorization Measures and their Implications in Large Language Models
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
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2025
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| _version_ | 1866915401098592256 |
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| author | Ghosh, Bishwamittra Das, Soumi Wu, Qinyuan Khan, Mohammad Aflah Gummadi, Krishna P. Terzi, Evimaria Garg, Deepak |
| author_facet | Ghosh, Bishwamittra Das, Soumi Wu, Qinyuan Khan, Mohammad Aflah Gummadi, Krishna P. Terzi, Evimaria Garg, Deepak |
| contents | Concerned with privacy threats, memorization in LLMs is often seen as undesirable, specifically for learning. In this paper, we study whether memorization can be avoided when optimally learning a language, and whether the privacy threat posed by memorization is exaggerated or not. To this end, we re-examine existing privacy-focused measures of memorization, namely recollection-based and counterfactual memorization, along with a newly proposed contextual memorization.
Relating memorization to local over-fitting during learning, contextual memorization aims to disentangle memorization from the contextual learning ability of LLMs. Informally, a string is contextually memorized if its recollection due to training exceeds the optimal contextual recollection, a learned threshold denoting the best contextual learning without training. Conceptually, contextual recollection avoids the fallacy of recollection-based memorization, where any form of high recollection is a sign of memorization. Theoretically, contextual memorization relates to counterfactual memorization, but imposes stronger conditions. Memorization measures differ in outcomes and information requirements.
Experimenting on 18 LLMs from 6 families and multiple formal languages of different entropy, we show that (a) memorization measures disagree on memorization order of varying frequent strings, (b) optimal learning of a language cannot avoid partial memorization of training strings, and (c) improved learning decreases contextual and counterfactual memorization but increases recollection-based memorization. Finally, (d) we revisit existing reports of memorized strings by recollection that neither pose a privacy threat nor are contextually or counterfactually memorized. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_14777 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Rethinking Memorization Measures and their Implications in Large Language Models Ghosh, Bishwamittra Das, Soumi Wu, Qinyuan Khan, Mohammad Aflah Gummadi, Krishna P. Terzi, Evimaria Garg, Deepak Machine Learning Concerned with privacy threats, memorization in LLMs is often seen as undesirable, specifically for learning. In this paper, we study whether memorization can be avoided when optimally learning a language, and whether the privacy threat posed by memorization is exaggerated or not. To this end, we re-examine existing privacy-focused measures of memorization, namely recollection-based and counterfactual memorization, along with a newly proposed contextual memorization. Relating memorization to local over-fitting during learning, contextual memorization aims to disentangle memorization from the contextual learning ability of LLMs. Informally, a string is contextually memorized if its recollection due to training exceeds the optimal contextual recollection, a learned threshold denoting the best contextual learning without training. Conceptually, contextual recollection avoids the fallacy of recollection-based memorization, where any form of high recollection is a sign of memorization. Theoretically, contextual memorization relates to counterfactual memorization, but imposes stronger conditions. Memorization measures differ in outcomes and information requirements. Experimenting on 18 LLMs from 6 families and multiple formal languages of different entropy, we show that (a) memorization measures disagree on memorization order of varying frequent strings, (b) optimal learning of a language cannot avoid partial memorization of training strings, and (c) improved learning decreases contextual and counterfactual memorization but increases recollection-based memorization. Finally, (d) we revisit existing reports of memorized strings by recollection that neither pose a privacy threat nor are contextually or counterfactually memorized. |
| title | Rethinking Memorization Measures and their Implications in Large Language Models |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.14777 |