Which Retain Set Matters for LLM Unlearning? A Case Study on Entity Unlearning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are prone to retaining unauthorized or sensitive information from their training data, which raises privacy concerns. |
| Approach: | They propose to use a group of queries that share similar syntactic structures with the data targeted for removal to investigate the effects of unlearning on various subsets of the retain set. |
| Outcome: | The proposed method reduces the retention set, the portion of training data that is not targeted for removal, and improves model performance across subsets. |
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| Challenge: | Existing studies have focused on instance-level unlearning, specifically removing predefined instances containing sensitive content. |
| Approach: | They propose a task to erase entity-related knowledge from the target model completely by analyzing the forget set and its size. |
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Unlearn What You Want to Forget: Efficient Unlearning for LLMs (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) can be used to memorize a vast amount of data, but can suffer from privacy issues and data protection violations. |
| Approach: | They propose an efficient unlearning framework that could update LLMs without retraining them . they introduce lightweight unlearning layers learned with a selective teacher-student objective into transformers . |
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Towards Robust Evaluation of Unlearning in LLMs via Data Transformations (2024.findings-emnlp)
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Abhinav Joshi, Shaswati Saha, Divyaksh Shukla, Sriram Vema, Harsh Jhamtani, Manas Gaur, Ashutosh Modi
| Challenge: | Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. |
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Not Every Token Needs Forgetting: Selective Unlearning Balancing Forgetting and Utility in Large Language Models (2025.findings-emnlp)
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| Challenge: | Conventional unlearning approaches forget all tokens in a target document, including common tokens that carry general knowledge. |
| Approach: | They propose a method that identifies a critical subset of tokens within the forgetting set that is relevant to the unwanted information and unlearns only those tokens. |
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UNLEARN Efficient Removal of Knowledge in Large Language Models (2025.findings-naacl)
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| Challenge: | Large Language Models excel in many tasks but are outperformed by specialized tools for certain tasks. |
| Approach: | They propose a method that uses subspace techniques to selectively remove knowledge . they propose 'unlearn' method that can forget or unlear the knowledge without retraining . |
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Learning to Refuse: Towards Mitigating Privacy Risks in LLMs (2025.coling-main)
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| Challenge: | Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural languages, but can inadvertently memorize private information, posing significant privacy risks. |
| Approach: | They propose to use a dataset to evaluate machine unlearning methods for protecting personal data in a realistic scenario. |
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Identifying Unlearned Data in LLMs via Membership Inference Attacks (2025.emnlp-main)
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| Challenge: | Existing work evaluates approximate unlearning under a retrieval paradigm, where adversaries attempt to extract residual knowledge given partial information of the unlearning target. |
| Approach: | They propose a framework to evaluate unlearning membership attacks using member inference techniques to exploit the forget set. |
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Reveal and Release: Iterative LLM Unlearning with Self-generated Data (2025.findings-emnlp)
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| Challenge: | Existing approaches to unlearning large language models assume full access to the forget dataset, overlooking two key challenges: (1) Forget data is often privacy-sensitive, rare, or legally regulated, making it expensive or impractical to obtain (2) The distribution of available forget data may not align with how that information is represented within the model. |
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Does Data Contamination Detection Work (Well) for LLMs? A Survey and Evaluation on Detection Assumptions (2025.findings-naacl)
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| Challenge: | Large language models have demonstrated great performance across various benchmarks, but data contamination is a concern in their evaluation. |
| Approach: | They analyze 50 papers on data contamination detection and test three of them as case studies to identify the possibility of data contamination. |
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Machine Unlearning of Pre-trained Large Language Models (2024.acl-long)
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| Challenge: | Using curated datasets, we establish a robust benchmark for unlearning performance, demonstrating that these methods are over 105 times more computationally efficient than retraining. |
| Approach: | They propose a framework for machine unlearning in pre-trained LLMs and integrate gradient ascent with gradient descent on in-distribution data to achieve robustness. |
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