| { |
| "schema_version": 2, |
| "title": "Reproduction: How much can language models memorize?", |
| "emoji": "🎯", |
| "space_id": "Varshith19/repro-how-much-can-language-models-memorize", |
| "paper": null, |
| "tags": [ |
| "icml2026-repro", |
| "paper-bA6BgSbaUi" |
| ], |
| "updated_at": "2026-07-22T12:49:11+00:00", |
| "root": { |
| "slug": "index", |
| "title": "Reproduction: How much can language models memorize?", |
| "file": "pages/index.md", |
| "children": [ |
| { |
| "slug": "executive-summary", |
| "title": "Executive summary", |
| "file": "pages/executive-summary/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-1-gpt-style-transformers-trained-on-uniform-random-data-show-an-empirical-memorization-capacity-plateau-of-about-3-6-bits-per-parameter-figure-1", |
| "title": "Claim 1: GPT-style transformers trained on uniform random data show an empirical memorization-capacity plateau of about 3.6 bits per parameter (Figure 1)", |
| "file": "pages/claim-1-gpt-style-transformers-trained-on-uniform-random-data-show-an-empirical-memorization-capacity-plateau-of-about-3-6-bits-per-parameter-figure-1/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-2-capacity-estimates-across-model-widths-and-depths-support-a-roughly-linear-bits-per-parameter-scaling-law-with-bfloat16-to-float32-increasing-capacity-only-modestly-table-1", |
| "title": "Claim 2: Capacity estimates across model widths and depths support a roughly linear bits-per-parameter scaling law, with bfloat16 to float32 increasing capacity only modestly (Table 1)", |
| "file": "pages/claim-2-capacity-estimates-across-model-widths-and-depths-support-a-roughly-linear-bits-per-parameter-scaling-law-with-bfloat16-to-float32-increasing-capacity-only-modestly-table-1/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-3-on-text-data-unintended-memorization-rises-with-model-size-but-decreases-once-models-begin-generalizing-relative-to-an-oracle-reference-model-figure-2", |
| "title": "Claim 3: On text data, unintended memorization rises with model size but decreases once models begin generalizing relative to an oracle reference model (Figure 2)", |
| "file": "pages/claim-3-on-text-data-unintended-memorization-rises-with-model-size-but-decreases-once-models-begin-generalizing-relative-to-an-oracle-reference-model-figure-2/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-4-double-descent-begins-when-dataset-information-content-exceeds-estimated-model-capacity-in-both-synthetic-bitstrings-and-text-experiments-figures-3-and-4", |
| "title": "Claim 4: Double descent begins when dataset information content exceeds estimated model capacity in both synthetic bitstrings and text experiments (Figures 3 and 4)", |
| "file": "pages/claim-4-double-descent-begins-when-dataset-information-content-exceeds-estimated-model-capacity-in-both-synthetic-bitstrings-and-text-experiments-figures-3-and-4/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "claim-5-the-paper-derives-and-evaluates-scaling-law-predictions-for-membership-inference-as-a-function-of-model-capacity-and-dataset-size-figure-7", |
| "title": "Claim 5: The paper derives and evaluates scaling-law predictions for membership inference as a function of model capacity and dataset size (Figure 7)", |
| "file": "pages/claim-5-the-paper-derives-and-evaluates-scaling-law-predictions-for-membership-inference-as-a-function-of-model-capacity-and-dataset-size-figure-7/page.md", |
| "children": [] |
| }, |
| { |
| "slug": "conclusion", |
| "title": "Conclusion", |
| "file": "pages/conclusion/page.md", |
| "children": [] |
| } |
| ] |
| }, |
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| "repo_id": "Varshith19/repro-how-much-can-language-models-memorize-traces", |
| "repo_type": "dataset", |
| "repo_url": "https://huggingface.co/datasets/Varshith19/repro-how-much-can-language-models-memorize-traces", |
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