REMIND: Memorization and Unlearning in LLMs Through the Lens of Input Loss Landscapes (2026.acl-long)
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| Challenge: | REMIND is a framework that diagnoses residual memorization states by probing local ILL curvature over semantically coherent neighborhoods. |
| Approach: | They propose a framework that diagnoses memorization states by probing local ILL curvature over semantically coherent neighborhoods. |
| Outcome: | The proposed framework outperforms baseline models with 82% multi-class ROC-AUC and 2 higher AUC at 1% FPR. |
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| Challenge: | Catastrophic Forgetting (CF) compromises the effectiveness of large language models during fine-tuning, yet the underlying causes of CF remain largely unexplored. |
| Approach: | They propose a method to flatten the model loss landscape to mitigate CF by flattening the loss landscape. |
| Outcome: | The proposed method complements existing anti-forgetting strategies, further enhancing the resistance of LLMs to CF. |
Representation-Guided Parameter-Efficient LLM Unlearning (2026.findings-acl)
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| Challenge: | Existing methods to unlearning large language models often memorize sensitive or harmful information, but they struggle with the forget-retain trade-off due to the polysemantic nature of LLMs parameters. |
| Approach: | They propose a representation-guided low-rank unlearning approach that leverages the geometric properties of representation spaces to achieve robust and precise unlearning. |
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Answer When Needed, Forget When Not: Language Models Pretend to Forget via In-Context Knowledge Unlearning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly required to selectively unlearn specific information. |
| Approach: | They propose a method which fine-tunes pre-trained LLMs to enable prompt unlearning of target knowledge within the context while preserving unrelated information. |
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Retracing the Past: LLMs Emit Training Data When They Get Lost (2025.emnlp-main)
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| Challenge: | Existing methods for extracting training data from large language models exhibit limited success . existing methods offer limited insight into the fundamental drivers of memorization leakage . |
| Approach: | They propose a framework for extracting memorized data by maximizing model uncertainty . they propose mismatched fine-tuning to weaken alignment and induce confusion . |
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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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RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)
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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang
| Challenge: | Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting. |
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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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Exploring Forgetting in Large Language Model Pre-Training (2025.acl-long)
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| Challenge: | Existing research on task-level forgetting in LLMs has focused on pretraining . but, there is limited attention to finer-grained forgetting during training . |
| Approach: | They investigated the existence and measurement of forgetting in pre-training . they examined low-cost, straightforward methods to mitigate forgetting during the pre- training phase . |
| Outcome: | The proposed methods could be used to mitigate forgetting during the pre-training phase and offer insights into the dynamics of forgetting. |
The Model Agreed, But Didn’t Learn: Diagnosing Surface Compliance in Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models internalize vast world knowledge as parametric memory, yet inherit the staleness and errors of their source corpora. |
| Approach: | They propose a framework that subjects models to discriminative self-assessment under diverse contextual pressures to scrutinize subtle behavioral nuances induced by memory modifications. |
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