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arxiv: 2606.18473 · v1 · pith:4MWPJK5Lnew · submitted 2026-06-16 · 💻 cs.CL

PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning

Bo Su , Ankit Shah , Thai Le This is my paper

Pith reviewed 2026-06-27 00:18 UTC · model grok-4.3

classification 💻 cs.CL
keywords machine unlearninglarge language modelscollateral damagedata geometryforget setsemantic distanceauditingLLM safety
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The pith

Interaction features between forget sets and evaluation sets can predict collateral damage from LLM unlearning before any model updates occur.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper studies how unlearning specified knowledge in large language models affects both nearby and distant information in the model. It documents a decay pattern in which collateral damage is strongest close to the forget set, weakens as semantic distance grows, yet continues across domain boundaries. The authors then frame forget-set auditing as a prediction task that uses pre-unlearning data features to forecast this damage. They find that interaction features between the forget set and evaluation sets give the strongest predictive signals. This positions data geometry as an early indicator that could flag risky unlearning operations without first running them.

Core claim

We find a consistent decay pattern: collateral damage is strongest near the forget set, weakens with semantic distance, but does not disappear at domain boundaries. Interaction features between the forget set and evaluation set provide the strongest signals, suggesting that collateral damage is partly reflected in data geometry before model updates occur.

What carries the argument

Forget-set auditing as a pre-unlearning prediction task that extracts interaction features between the forget set and evaluation sets to forecast downstream collateral damage.

If this is right

  • Unlearning runs can be screened in advance by computing interaction features to identify those likely to cause high collateral damage.
  • Semantic distance from the forget set can be used to anticipate how far damage will propagate.
  • Evaluation sets can be chosen or adjusted based on their geometric interaction with the forget set to reduce unexpected effects.
  • Unlearning procedures can incorporate pre-checks on data geometry to improve overall reliability.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same interaction-based auditing might be tested on other model-editing operations such as targeted fine-tuning or knowledge editing.
  • Incorporating these features directly into unlearning algorithms could be explored as a way to adjust the forget process dynamically.
  • Similar pre-auditing could apply to safety interventions that modify model behavior without full retraining.

Load-bearing premise

Collateral damage patterns observed after unlearning can be reliably predicted in advance from static data features alone without performing any model update or training.

What would settle it

Running actual unlearning on a range of forget sets and finding that the strength of interaction features shows no correlation with the measured collateral damage on evaluation sets after the updates.

Figures

Figures reproduced from arXiv: 2606.18473 by Ankit Shah, Bo Su, Thai Le.

Figure 1
Figure 1. Figure 1: PreUnlearn framework with two stages:(1) Three-layer Unlearning Impact where an LLM is un￾learned on a candidate forget set Df and scored across three semantic layers (L1, L2, L3) of decreasing rele￾vancy to Df and ultimately, (2) Pre-unlearn Impact Auditing produces a lightweight auditor to estimate per￾layer potential collateral damage risk on retain knowl￾edge, screening the candidate Df before unlearni… view at source ↗
Figure 2
Figure 2. Figure 2: Dataset construction schema. WikiText-103 passages are filtered, embedded, clustered into semantic [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: PPL ratio distribution by layer across six settings. CDF of PPL ratio (after / before unlearning) for L1 (self), L2 (same domain), L3 (different domain). Dotted grey verticals mark per-layer medians; dashed line at ratio = 1 is the no-change reference. Target Unlearning Model. We use two open￾weight instruction-tuned target models: Llama-3.1- 8B-Instruct (Grattafiori et al., 2024) and Qwen2.5- 7B-Instruct … view at source ↗
Figure 4
Figure 4. Figure 4: Mean PPL ratio per domain pair on Llama-3.1-8B-Instruct under NPO and UNDIAL. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Best Spearman correlation across the three [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
read the original abstract

Machine unlearning for large language models (LLMs) aims to remove specified knowledge while preserving the rest of the model's capabilities. However, the boundary between knowledge to forget and knowledge to retain is often unclear, since related and even distant information may be entangled in the model. In this paper, we study LLM unlearning from a data-centric perspective and measure how unlearning effects propagate from the forget set to same-domain and distant-domain knowledge. We find a consistent decay pattern: collateral damage is strongest near the forget set, weakens with semantic distance, but does not disappear at domain boundaries. We further ask whether such damage can be audited before unlearning is executed. We formulate forget-set auditing as a pre-unlearning prediction task and analyze which data features are most predictive of downstream damage. Our results show that interaction features between the forget set and evaluation set provide the strongest signals, suggesting that collateral damage is partly reflected in data geometry before model updates occur. These findings position forget-set auditing as an early warning tool for identifying risky unlearning runs and designing more reliable unlearning procedures.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper studies collateral damage during LLM unlearning from a data-centric perspective. It reports a consistent decay pattern where damage is strongest near the forget set, weakens with semantic distance, and persists across domain boundaries. It then frames forget-set auditing as a pre-unlearning prediction task and claims that interaction features between the forget set and evaluation sets are the strongest predictors of downstream damage, implying that such damage is partly encoded in static data geometry before any model update occurs.

Significance. If the predictive relationship can be shown to hold prospectively on unseen forget/evaluation pairs without access to post-unlearning outcomes, the work would offer a practical early-warning tool for unlearning safety and could inform more robust unlearning design. The data-geometry angle is a useful complement to existing model-update analyses.

major comments (2)
  1. [Experimental setup / prediction task formulation] The central claim that interaction features enable pre-unlearning prediction of collateral damage is load-bearing for the contribution. The manuscript must demonstrate that feature selection, regression fitting, or importance ranking was performed without using the observed post-unlearning damage values as supervision; otherwise the reported predictive strength is retrospective rather than prospective. This directly affects whether the results support an 'early warning' capability (see abstract and the forget-set auditing formulation).
  2. [Results on predictive signals] The decay pattern and feature-predictiveness results require explicit held-out evaluation: the interaction features must be tested on forget/evaluation pairs that were never used to compute or select those features. Without such separation, the analysis risks circularity between the geometry used to define evaluation sets and the geometry used to predict damage on those sets.
minor comments (2)
  1. [Abstract] The abstract states the main findings but provides no information on datasets, unlearning methods, evaluation metrics, or statistical controls; these details are essential for assessing reproducibility even in a data-centric study.
  2. [Methods] Notation for 'interaction features' and 'semantic distance' should be defined precisely (e.g., via explicit formulas or embedding-based metrics) to allow independent replication.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed comments on the prospective nature of the prediction task. We address both major comments below and will revise the manuscript to provide the requested demonstrations of held-out, pre-unlearning prediction.

read point-by-point responses

  1. Referee: [Experimental setup / prediction task formulation] The central claim that interaction features enable pre-unlearning prediction of collateral damage is load-bearing for the contribution. The manuscript must demonstrate that feature selection, regression fitting, or importance ranking was performed without using the observed post-unlearning damage values as supervision; otherwise the reported predictive strength is retrospective rather than prospective. This directly affects whether the results support an 'early warning' capability (see abstract and the forget-set auditing formulation).

    Authors: We agree that a fully prospective formulation requires training the predictive model without access to post-unlearning damage on the evaluation pairs used for testing. The interaction features themselves are computed exclusively from static data geometry before any unlearning occurs. In the revision we will add an explicit cross-validation protocol: a regression model will be fit on a training partition of forget/evaluation pairs (using their post-unlearning damage only for supervision within that partition) and evaluated on completely disjoint held-out pairs whose damage values are never seen during feature selection or model fitting. This change will be documented in a new subsection on prospective auditing. revision: yes

  2. Referee: [Results on predictive signals] The decay pattern and feature-predictiveness results require explicit held-out evaluation: the interaction features must be tested on forget/evaluation pairs that were never used to compute or select those features. Without such separation, the analysis risks circularity between the geometry used to define evaluation sets and the geometry used to predict damage on those sets.

    Authors: We acknowledge the risk of circularity when the same data geometry informs both set construction and prediction. The revised manuscript will partition all evaluation sets into disjoint training and test groups for the prediction experiments. Feature selection, importance ranking, and regression fitting will be performed only on the training partition; all reported predictive metrics (including R² and feature rankings) will be computed exclusively on the held-out test pairs. This separation will be added to the experimental setup and results sections. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The provided abstract and text describe an empirical analysis that computes static data features (including interaction features) from forget and evaluation sets, then reports their correlation strength with observed collateral damage after unlearning runs. No equations, sections, or methodological descriptions are supplied that reduce the reported 'strongest signals' or 'pre-unlearning prediction' to a fitted parameter or self-referential definition of the damage labels themselves. The central claim remains an observational finding about data geometry rather than a tautological renaming or post-hoc fit presented as prospective prediction. No self-citation load-bearing steps or uniqueness theorems are invoked in the given material. The derivation is therefore self-contained against external benchmarks of damage measurement.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no information on free parameters, axioms, or invented entities used in the study.

pith-pipeline@v0.9.1-grok · 5715 in / 1048 out tokens · 33367 ms · 2026-06-27T00:18:35.975288+00:00 · methodology

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