OpenNovelty.org

LLM Unlearning with LLM Beliefs

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Large Language Model Unlearning
Abstract:

Large language models trained on vast corpora inherently risk memorizing sensitive or harmful content, which may later resurface in their outputs. Prevailing unlearning methods generally rely on gradient ascent and its variants to lower the probability of specific target responses. However, we find that this strategy induces a critical side effect: probability mass is redistributed into high-likelihood regions, often corresponding to semantically related rephrasings of the targets. We refer to this as the squeezing effect, which explains why many methods yield merely spurious unlearning, a problem further obscured by automated metrics (e.g., ROUGE, truth ratio) that misreport actual success. To address this, we propose a bootstrapping (BS) framework that explicitly links the squeezing effect with the model’s own high-confidence generations, namely its model beliefs. Since model beliefs inherently capture the very high-likelihood regions where probability mass is squeezed, incorporating them into the unlearning objective directly counters the squeezing effect. By jointly suppressing both target responses and model beliefs, BS-T (token) attenuates high-probability tokens, whereas BS-S (sequence) removes entire high-confidence generations, together achieving more thorough forgetting while preserving utility. Extensive experiments on diverse benchmarks confirm the effectiveness of our approach.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a bootstrapping framework that addresses the 'squeezing effect' in LLM unlearning by incorporating model beliefs—high-confidence generations that capture redistributed probability mass. It occupies a newly created taxonomy leaf ('Model Belief and Probability Redistribution') under gradient-based unlearning techniques, with no sibling papers currently in that leaf. This positioning suggests the work carves out a distinct methodological niche within the broader gradient-based unlearning landscape, which includes five papers in first-order methods and one in second-order approaches.

The taxonomy reveals that gradient-based unlearning sits alongside parameter-efficient methods (two leaves with seven papers total) and prompt-based techniques (three papers). The paper's focus on probability redistribution and belief modeling distinguishes it from neighboring first-order gradient methods that primarily use ascent or descent modifications without explicit belief tracking. The taxonomy's scope and exclude notes clarify that while this work uses gradients, its explicit modeling of high-likelihood regions separates it from general gradient ascent approaches in sibling categories.

Among nineteen candidates examined across three contributions, no refutable prior work was identified. The bootstrapping framework contribution examined ten candidates with zero refutations, while the theoretical analysis examined nine candidates, also with zero refutations. The squeezing effect characterization was not matched against any candidates. This limited search scope—focused on top-K semantic matches and citation expansion—suggests the specific combination of bootstrapping and model belief suppression may not have direct precedents in the examined literature, though the analysis does not claim exhaustive coverage of all related gradient-based or probabilistic unlearning work.

Given the constrained search of nineteen papers from a fifty-paper taxonomy, the framework's novelty appears plausible within the examined scope. The absence of refutable candidates across contributions, combined with the creation of a new taxonomy leaf, indicates the work may introduce a distinct perspective on addressing probability redistribution artifacts. However, the limited candidate pool means potentially relevant work in adjacent areas—such as probabilistic evaluation frameworks or distribution-based methods—may not have been fully explored.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
19
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: machine unlearning in large language models. The field has crystallized around several major branches that reflect both methodological diversity and the multifaceted nature of the problem. Unlearning Methods and Algorithms encompasses a range of techniques—from gradient-based approaches that redistribute model beliefs to parameter-efficient strategies and prompt-based interventions—each trading off computational cost against precision of forgetting. Evaluation Frameworks and Metrics address the challenge of measuring whether targeted knowledge has truly been removed without degrading overall model utility, while Unlearning Objectives and Problem Formulations explore different notions of what it means to forget, such as exact data removal versus concept erasure. Surveys, Taxonomies, and Conceptual Frameworks provide high-level organization of the rapidly growing literature, and branches on Forgetting Phenomena in LLM Training and Fine-Tuning examine unintended memory dynamics during standard model updates. Applications and Cross-Domain Studies extend unlearning to privacy, safety, and bias mitigation, and Related Paradigms and Theoretical Foundations connect machine unlearning to continual learning and probabilistic inference. Within the gradient-based methods, a particularly active line of work focuses on how models encode and can be made to revise their internal beliefs. LLM Beliefs Unlearning[0] sits squarely in this cluster, emphasizing model belief and probability redistribution to selectively weaken unwanted associations. This contrasts with works like Rethinking LLM Unlearning[1] and Closer Look Unlearning[3], which critically examine whether existing gradient techniques genuinely erase knowledge or merely suppress surface-level outputs. Meanwhile, Dissecting Fine-tuning Unlearning[5] and Pretrained LLM Unlearning[6] explore how fine-tuning stages interact with unlearning objectives, revealing that forgetting can be fragile or incomplete when models retain latent representations. A central open question across these studies is whether probabilistic reweighting offers more robust guarantees than simpler gradient ascent, and how to balance targeted erasure with the preservation of broader capabilities—a tension that LLM Beliefs Unlearning[0] addresses by focusing on controlled belief adjustment rather than wholesale parameter reversal.

Claimed Contributions

Identification and characterization of the squeezing effect in LLM unlearning

The authors identify a critical failure mode in existing gradient ascent-based unlearning methods where probability mass is redistributed into high-likelihood regions corresponding to semantically related rephrasings of target responses. They term this the squeezing effect and demonstrate it leads to spurious unlearning that is poorly captured by standard metrics.

0 retrieved papers
Bootstrapping framework incorporating model beliefs for unlearning

The authors introduce a bootstrapping framework that uses the model's own high-confidence predictions (model beliefs) as auxiliary unlearning signals. This is realized through BS-T, which suppresses high-probability tokens, and BS-S, which augments entire high-confidence sequences, directly counteracting the squeezing effect.

10 retrieved papers
Theoretical analysis of bootstrapping under learning dynamics framework

The authors provide theoretical analysis using the AKG learning dynamics framework to show how their bootstrapping approach reshapes the residual term in gradient updates, demonstrating how BS-T and BS-S spread forgetting pressure across both local belief neighborhoods and broader sequence-level alternatives.

9 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Identification and characterization of the squeezing effect in LLM unlearning

The authors identify a critical failure mode in existing gradient ascent-based unlearning methods where probability mass is redistributed into high-likelihood regions corresponding to semantically related rephrasings of target responses. They term this the squeezing effect and demonstrate it leads to spurious unlearning that is poorly captured by standard metrics.

Contribution

Bootstrapping framework incorporating model beliefs for unlearning

The authors introduce a bootstrapping framework that uses the model's own high-confidence predictions (model beliefs) as auxiliary unlearning signals. This is realized through BS-T, which suppresses high-probability tokens, and BS-S, which augments entire high-confidence sequences, directly counteracting the squeezing effect.

Contribution

Theoretical analysis of bootstrapping under learning dynamics framework

The authors provide theoretical analysis using the AKG learning dynamics framework to show how their bootstrapping approach reshapes the residual term in gradient updates, demonstrating how BS-T and BS-S spread forgetting pressure across both local belief neighborhoods and broader sequence-level alternatives.

Table of Contents

LLM Unlearning with LLM Beliefs | Novelty Validation