Writing study: Hubble: a Model Suite to Advance the Study of LLM Memorization
← ICLR 2026 oral writing corpus
Johnny Wei, Ameya Godbole, Mohammad Aflah Khan, Ryan Wang, Xiaoyuan Zhu, James Flemings, Nitya Kashyap, Krishna Gummadi, Willie Neiswanger, Robin Jia · Official ICLR 2026 oral record
Figure, table and section ledger
Editorial contribution type: Datasets, benchmarks & measurement resources (primary) · Empirical analysis, diagnosis & auditing.
Layout extraction, not a required paper template. Counts are distinct caption labels; a multi-panel figure counts once. Section spans are estimated from heading positions, including floats and whitespace. Method and limitations.
- Canonical PDF
- 62 physical pages
- Before references / appendix
- ≈ 10.46 page-equivalents
- Main figures / tables
- 3 / 1
- Attached appendix figures / tables
- 20 / 14
- Attached appendix span
- ≈ 38.87 page-equivalents
Section-by-section lengths and source pages
The title/abstract/front matter precede the first numbered heading. A span ends at the next detected top-level heading, not at the last sentence; end-page whitespace is included. References and statements are listed separately. These are not measurements of text-only or artwork area.
| Top-level heading | Starts on PDF page | Estimated span | Share of entire PDF |
|---|---|---|---|
| 1 INTRODUCTION | 1 | ≈ 0.97 pp. | 1.56% |
| 2 PERTURBATION DESIGN ACROSS RISK DOMAINS | 2 | ≈ 1.82 pp. | 2.94% |
| 3 THE HUBBLE SUITE | 4 | ≈ 3.18 pp. | 5.13% |
| 4 DOMAIN-AGNOSTIC RESULTS | 7 | ≈ 1 pp. | 1.61% |
| 5 DOMAIN-SPECIFIC RESULTS | 8 | ≈ 0.51 pp. | 0.82% |
| 6 USE CASES OF HUBBLE | 9 | ≈ 1.6 pp. | 2.58% |
| 7 DISCUSSION AND CONCLUSION | 10 | ≈ 0.4 pp. | 0.65% |
| ACKNOWLEDGMENTS | 11 | ≈ 0.35 pp. | 0.56% |
| REFERENCES | 11 | ≈ 12.67 pp. | 20.44% |
| Appendix | 24 | ≈ 0.97 pp. | 1.56% |
| A PERTURBATIONS | 25 | ≈ 4 pp. | 6.45% |
| B TRAINING | 29 | ≈ 2 pp. | 3.23% |
| C GENERAL EVALUATION | 31 | ≈ 3 pp. | 4.84% |
| D DOMAIN-SPECIFIC RESULTS | 34 | ≈ 14 pp. | 22.58% |
| E ADDITIONAL RESULTS | 48 | ≈ 5 pp. | 8.06% |
| F ADDITIONAL HUBBLEMIA RESULTS | 53 | ≈ 4 pp. | 6.45% |
| G ADDITIONAL HUBBLEUNLEARNING RESULTS | 57 | ≈ 3 pp. | 4.84% |
| H FURTHER DISCUSSION | 60 | ≈ 1 pp. | 1.61% |
| I ADDITIONAL PLOTS | 61 | ≈ 1.89 pp. | 3.05% |
All detected figure and table locations
Attached appendix
- Table 2 · p. 25
- Table 3 · p. 27
- Table 4 · p. 27
- Table 5 · p. 29
- Table 6 · p. 31
- Table 7 · p. 32
- Table 8 · p. 33
- Figure 4 · p. 35
- Figure 5 · p. 35
- Table 9 · p. 36
- Figure 6 · p. 38
- Figure 7 · p. 39
- Figure 8 · p. 40
- Table 10 · p. 41
- Figure 9 · p. 43
- Figure 10 · p. 45
- Figure 11 · p. 46
- Figure 12 · p. 47
- Figure 13 · p. 48
- Figure 14 · p. 49
- Figure 15 · p. 51
- Figure 16 · p. 52
- Table 11 · p. 53
- Table 12 · p. 53
- Table 13 · p. 54
- Table 14 · p. 54
- Figure 17 · p. 55
- Figure 18 · p. 55
- Figure 19 · p. 56
- Table 15 · p. 57
- Figure 20 · p. 58
- Figure 21 · p. 59
- Figure 22 · p. 61
- Figure 23 · p. 62
Argument and organization
- Risk domains to controlled perturbations
Translates copyright, privacy and contamination questions into randomized experimental interventions.
- Model suite, cross-domain findings and reusable benchmarks
Separates the research artifact from illustrative memorization, membership-inference and unlearning studies.
Problem, claim and contribution
The introduction identifies a methodological gap between small controlled studies and large observational studies, then explains why a released intervention suite closes it.
Section 1, pages 1–2. · Main paper
Argument and section order
The model runs are organized by research question—core factorial design, interference, timing, paraphrases and architecture—rather than presented as an undifferentiated checkpoint list.
Section 3.2, pages 5–6. · Main paper
Figures, captions and visual explanation
Figure 1 visualizes insertion boundaries, EOS delimiters and sequence resizing, and its caption explicitly discloses how perturbations differ from ordinary documents.
Figure 1, page 5. · Main paper
Tables, comparisons and evidence
Figure 3 marks the perturbed model, standard model and desired selective-unlearning target, with arrows indicating improvement. This makes utility loss and forgetting visible simultaneously.
Figure 3, page 10. · Main paper
Appendix and reproducibility
The appendix supplies dataset links, duplicate counts, attack templates and grids; notably, it reports that ELLie minimal-pair overlap invalidates that dataset for the dilution analysis.
Appendices A, D.2–D.3 and G; pages 25–28, 36–45 and 57. · Main paper
Clarity and writing conventions
The evaluation section labels attacks as lower bounds on memorization, resisting the common inference that failed extraction proves absence of stored information.
Section 3.3, page 6; Appendix D.2.1, page 37. · Main paper
What to reuse—and what not to infer
Transferable lesson: For a benchmark or model-suite paper, connect each artifact variation to a causal question, make interventions inspectable, and publish invalidated analyses as well as favorable findings.
Boundary: Dilution and early ordering are empirical risk-reduction findings, not privacy guarantees or legal safe harbors. Appendix ROC captions reverse member/non-member labels relative to the stated protocol; hyperparameter epsilon values also differ between Table 5 and the prose, so reuse the released configuration rather than silently resolving discrepancies.
Apply this lesson using the argument and evidence worksheet.
Inspection record and sources
- Checked
- 2026-09-19
- Reading status
- Full text + selected visuals inspected
- Main text
- Pages 1–11, Sections 1–7 and acknowledgments; bibliographic references separately listed on pages 11–23.
- Appendix / supplement
- Pages 24–62, Appendices A–I, including data generation, decontamination, configurations, domain analyses, benchmark results and all plot captions.
- Visually inspected PDF pages
- 5, 10