Mr. Snuffleupagus at S em E val-2025 Task 4: Unlearning Factual Knowledge from LLM s Using Adaptive RMU (2025)

Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, their tendency to memorize training data raises concerns regarding privacy, copyright compliance, and security, particularly in cases involving Personally Identifiable Information (PII). Effective machine unlearning techniques are essential to mitigate these risks, yet existing methods remain underdeveloped for LLMs due to their open-ended output space. In this work, we apply the Adaptive Representation Misdirection Unlearning (RMU) technique to unlearn sensitive information from LLMs. Through extensive experiments, we analyze the effects of unlearning across different decoder layers to determine the most effective regions for sensitive information removal. Our technique ranked 4th on the official leaderboard of both 1B parameter and 7B parameter models.

## introduction in the realm of large language models (llms), unlearning is particularly challenging due to the highly distributed nature of knowledge storage across model parameters. unlike computer vision or graph neural networks @xcite , where feature representations tend to be more localized, llms encode knowledge in an interwoven manner, making targeted removal complex @xcite . the need for effective llm unlearning arises from concerns surrounding data privacy, bias mitigation, and compliance with regulations such as gdpr's 'right to be forgotten'. if an llm generates sensitive or misleading information, it is crucial to develop mechanisms that remove such knowledge without degrading overall performance @xcite ). an approach to tackling unlearning in llms involves model-editing, a technique closely tied to mechanistic interpretability. model-editing methods aim to modify the internal representations of an llm to suppress or alter specific outputs while maintaining overall fluency and coherence. previous work has explored various approaches, such as neuron pruning @xcite and gradient-based forgetting @xcite , to achieve unlearning without full retraining. unlearning techniques for llms often lead to catastrophic forgetting, compromising general performance @xcite @xcite . understanding how and where knowledge is stored in an llm is crucial for designing effective unlearning strategies. research has shown that different decoder layers in transformer-based architectures capture different types of information. the task is outlined in the task description paper @xcite . there are separate leaderboards for the 1b and 7b parameter models. participating in this task provided valuable insights into both the strengths and limitations of our system. quantitatively, our approach achieved competitive results, ranking 4th among participating teams on a metric designed specifically for this task. additionally, we analyze how factual information is distributed across different decoder layers in large language models (llms). through our experiments, we demonstrate that unlearning factual information from middle-later layers is particularly effective. we have released the code for our system on github foot_0 , facilitating transparency and reproducibility in our approach. ## related works effective unlearning techniques focus on modifying model representations to diminish the influence of specific data while preserving overall performance. these methods often involve fine-tuning strategies that steer internal representations away from unwanted content, ensuring that the model forgets particular information without compromising its general capabilities. one prominent approach is representation misdirection unlearning (rmu) @xcite , which directs the model's intermediate representations of data intended for unlearning toward a predetermined random vector. this technique effectively reduces the model's performance on tasks related to the forgotten content while maintaining its proficiency in other domains. rmu has been demonstrated to lower the model's knowledge of the weapons of mass destruction proxy (wmdp) dataset, indicating its potential in reducing malicious use of llms @xcite . building upon rmu, adaptive rmu introduces a dynamic adjustment mechanism for the steering coefficient, which influences the alignment of forget-sample representations with the random direction @xcite . this adaptive approach enhances unlearning effectiveness across various network layers, addressing limitations observed when rmu is applied to middle and later layers of llms. adaptive rmu not only improves unlearning performance but also maintains robustness against adversarial attacks, ensuring that the model does not inadvertently relearn the forgotten content. another innovative technique involves the use of sparse autoencoders (saes) to remove specific knowledge from llms @xcite . by training saes to capture and subsequently eliminate features associated with the content to be unlearned, this method effectively reduces the model's ability to recall unwanted information. studies have shown that applying saes can unlearn subsets of sensitive data with minimal impact on the model's performance in other areas, offering a targeted and efficient unlearning strategy. ## results we evaluated the performance of our unlearned model using the evaluation metric described in section 3.3. ## conclusion in this work, we demonstrate that applying the adaptive rmu (rank-one model update) technique to later decoder layers is an effective strategy for unlearning factual information from large language models (llms). a key advantage of our method is its minimal hyperparameter tuning requirement, making it easily adaptable to different llm architectures. our technique achieved 4th place on the official leaderboard for both 1b and 7b parameter models, highlighting its competitiveness among various unlearning approaches. while the original rmu implementation demonstrated that unlearning from earlier layers effectively removes hazardous knowledge-such as information related to cybersecurity threats and bioweapons, which require deep contextual understanding-we extend this research by showing that factual knowledge (e.g., phone numbers, social security numbers, and addresses) is more effectively unlearned from later decoder layers. despite these promising results, unlearning sensitive content from llms remains an open challenge, with several promising directions for future research. for instance, further exploration is needed to assess the trade-offs between unlearning effectiveness and generalization, particularly when removing factual knowledge versus conceptual reasoning. overall, our work contributes to the broader fields of model editing and mechanistic inter-pretability, providing valuable insights into the layerwise dynamics of unlearning and paving the way for more robust and efficient strategies in the future.