Paper Citation Record · LEDGER

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning

As of 16 August 2026, this Paper Citation Record lists 82 of 82 outbound references and 3 inbound Pith citation observations for arXiv:2505.11953.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.11953 v2

Coverage vector

measured 82 of 82 reference resolution

Typed states for the displayed outbound observations.

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measured 85 of 85 standing notices

One-hop event checks from named stored sources.

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measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-18T10:46:17.087522Z

Reference resolution

82 of 82 outbound references displayed

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External citation measurements

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Outbound references

Observation b0e96e82-db50-4628-9a47-e0a3b334f3cc · outbound

This paper cites GPT-4 Technical Report.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning GPT-4 Technical Report

Reference 1

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This paper cites The CRINGE Loss: Learning what language not to model.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning The CRINGE Loss: Learning what language not to model

Reference 2

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This paper cites Harnessing Business and Media Insights with Large Language Models.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Harnessing Business and Media Insights with Large Language Models

Reference 3

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This paper cites R., Christodorescu, M., Datta, A., Feizi, S., et al.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning R., Christodorescu, M., Datta, A., Feizi, S., et al

Reference 4

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This paper cites Soft Prompting for Unlearning in Large Language Models.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Soft Prompting for Unlearning in Large Language Models

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This paper cites A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N

Reference 6

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning and Lipton, Z

Reference 7

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Observation d4afebd5-1ca5-4142-a2b5-404f7bb85d83 · outbound

This paper cites Unlearn What You Want to Forget: Efficient Unlearning for LLMs.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Unlearn What You Want to Forget: Efficient Unlearning for LLMs

Reference 8

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This paper cites Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport

Reference 9

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This paper cites Label Smoothing Improves Machine Unlearning.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Label Smoothing Improves Machine Unlearning

Reference 10

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This paper cites UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models

Reference 11

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Observation b1736397-ba8e-4781-b5d3-7a7f834f1860 · outbound

This paper cites Who's Harry Potter? Approximate Unlearning in LLMs.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Who's Harry Potter? Approximate Unlearning in LLMs

Reference 12

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This paper cites KTO: Model Alignment as Prospect Theoretic Optimization.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning KTO: Model Alignment as Prospect Theoretic Optimization

Reference 13

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This paper cites SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation

Reference 14

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This paper cites Simplicity prevails: Rethinking negative preference optimization for llm unlearning.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Simplicity prevails: Rethinking negative preference optimization for llm unlearning

Reference 15

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This paper cites Challenging forgets: Unveiling the worst-case forget sets in machine unlearning.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Challenging forgets: Unveiling the worst-case forget sets in machine unlearning

Reference 16

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This paper cites On Large Language Model Continual Unlearning.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning On Large Language Model Continual Unlearning

Reference 17

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This paper cites General data protection regulation.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning General data protection regulation

Reference 18

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This paper cites Eternal sunshine of the spotless net: Selective forgetting in deep networks.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Eternal sunshine of the spotless net: Selective forgetting in deep networks

Reference 19

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Amnesiac machine learning

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning MEOW: MEMOry Supervised LLM Unlearning Via Inverted Facts

Reference 21

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Aligning ai with shared human values

Reference 22

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Measuring massive multitask language understanding

Reference 23

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Offset Unlearning for Large Language Models

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning TrustLLM: Trustworthiness in Large Language Models

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Robust generalization against photon-limited corruptions via worst-case sharpness minimization

Reference 26

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Editing Models with Task Arithmetic

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning A., Chaudhuri, K., and Zou, J

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Knowledge Unlearning for Mitigating Privacy Risks in Language Models

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Reversing the Forget-Retain Objectives: An Efficient LLM Unlearning Framework from Logit Difference

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Mistral 7B

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Copyright Violations and Large Language Models

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning DeepInception: Hypnotize Large Language Model to Be Jailbreaker

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Textbooks Are All You Need II: phi-1.5 technical report

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Large Language Model Unlearning via Embedding-Corrupted Prompts

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Model sparsity can simplify machine unlearning

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Human and AI Perceptual Differences in Image Classification Errors

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Rethinking Machine Unlearning for Large Language Models

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning and Guo, H

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning C., and Kolter, J

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Alternate Preference Optimization for Unlearning Factual Knowledge in Large Language Models

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning More human than human: measuring chatgpt political bias

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning K., Shokri, R., and Theodorakopoulos, G

Reference 46

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning R., and Papernot, N

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Unresolved cited work

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Can Sensitive Information Be Deleted From LLMs? Objectives for Defending Against Extraction Attacks

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning In-Context Unlearning: Language Models as Few Shot Unlearners

Reference 50

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Safety alignment should be made more than just a few tokens deep

Reference 51

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning D., Ermon, S., and Finn, C

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning White-box vs black-box: Bayes optimal strategies for membership inference

Reference 53

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Detecting Pretraining Data from Large Language Models

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning MUSE: Machine Unlearning Six-Way Evaluation for Language Models

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Membership inference attacks against machine learning models

Reference 56

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This paper cites Guardrail Baselines for Unlearning in LLMs.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Guardrail Baselines for Unlearning in LLMs

Reference 57

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Unrolling sgd: Understanding factors influencing machine unlearning

Reference 58

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 59

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This paper cites KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment

Reference 60

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This paper cites Learning to augment distributions for out-of-distribution detection.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Learning to augment distributions for out-of-distribution detection

Reference 61

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This paper cites A sober look at the robustness of clips to spurious features.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning A sober look at the robustness of clips to spurious features

Reference 62

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This paper cites Towards effective evaluations and comparison for llm unlearning methods.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Towards effective evaluations and comparison for llm unlearning methods

Reference 63

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning P., Zhou, Z., Shin, S., Han, B., and Weinberger, K

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:48:53.900476Z

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This paper cites Self-Instruct: Aligning Language Models with Self-Generated Instructions.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Self-Instruct: Aligning Language Models with Self-Generated Instructions

Reference 65

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning LLM Unlearning via Loss Adjustment with Only Forget Data

Reference 66

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Gru: Mitigating the trade-off between unlearning and retention for large language models

Reference 67

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This paper cites Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations

Reference 68

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning V., Zhou, D., et al

Reference 69

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This paper cites Trustworthy graph learning: Reliability, explainability, and privacy protection.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Trustworthy graph learning: Reliability, explainability, and privacy protection

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:48:53.752114Z

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Observation 1929c9a2-bdd9-46de-a94f-d1d990e3ead2 · outbound

This paper cites Adaptive localization of knowledge negation for continual llm unlearning.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Adaptive localization of knowledge negation for continual llm unlearning

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:48:53.735196Z

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This paper cites Large Language Model Unlearning.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Large Language Model Unlearning

Reference 72

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Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning K., Bindschaedler, V., and Shokri, R

Reference 73

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This paper cites Towards safe machine unlearning: a paradigm that mitigates performance degradation.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Towards safe machine unlearning: a paradigm that mitigates performance degradation

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:48:53.658573Z

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This paper cites Unlearning bias in language models by partitioning gradients.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Unlearning bias in language models by partitioning gradients

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:48:53.516847Z

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Observation a8975dba-6b2e-48d1-bc06-7a15e1b72a5d · outbound

This paper cites Mind the label shift of augmentation-based graph ood generalization.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Mind the label shift of augmentation-based graph ood generalization

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:48:53.446504Z

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Observation 9094edfc-01a5-44d0-a5a7-7b1979fa4593 · outbound

This paper cites Thought propagation: An analogical approach to complex reasoning with large language models.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Thought propagation: An analogical approach to complex reasoning with large language models

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:48:53.429007Z

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Observation 654235b4-1ce4-4a33-b280-880a981f0c85 · outbound

This paper cites Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Negative Preference Optimization: From Catastrophic Collapse to Effective Unlearning

Reference 78

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Observation 51e13328-4318-4f07-8d6b-99cfc0859d22 · outbound

This paper cites Can language models perform robust reasoning in chain-of-thought prompting with noisy rationales? In NeurIPS, 2024.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Can language models perform robust reasoning in chain-of-thought prompting with noisy rationales? In NeurIPS, 2024

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:48:53.403334Z

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Observation 657c8ae0-09d2-464d-a296-5ead5dcb7d28 · outbound

This paper cites Landscape of thoughts: Visualizing the reasoning process of large language models.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Landscape of thoughts: Visualizing the reasoning process of large language models

Reference 80

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This paper cites Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models

Reference 81

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This paper cites write newline.

Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning write newline

Reference 82

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Pith citing papers

Observation 0cc44bcf-5c31-4223-9f34-610e304e85a8 · inbound

Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning cites this paper.

Downgrade to Upgrade: Optimizer Simplification Enhances Robustness in LLM Unlearning Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning

Reference 26

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Observation 7a5e48f0-a056-4a63-ae81-e35adacaea11 · inbound

A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning cites this paper.

A Mechanistic Perspective and Circuit-Guided Difficulty Metric for Unlearning Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning

Reference 50

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MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models cites this paper.

MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models Exploring Criteria of Loss Reweighting to Enhance LLM Unlearning

Reference 13

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