CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing
arXiv:2603.19297v1 Announce Type: new Abstract: The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing techniques offer a promising solution by modifying a model's factual associations, they often produce unpredictable ripple effects, which are unintended behavioral changes that propagate even to the hidden space. In this work, we introduce CLaRE, a lightweight representation-level technique to identify where these ripple effects may occur. Unlike prior gradient-based methods, CLaRE quantifies entanglement between facts using forward activations from a single intermediate layer, avoiding costly backward passes. To enable systematic study, we prepare and analyse a corpus of 11,427 facts drawn from three existing datasets. Using CLaRE, we compute large-scale entanglement graphs of this corpus for multiple models, capturing how local edits propagate through representational space. These graphs enable str
arXiv:2603.19297v1 Announce Type: new Abstract: The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing techniques offer a promising solution by modifying a model's factual associations, they often produce unpredictable ripple effects, which are unintended behavioral changes that propagate even to the hidden space. In this work, we introduce CLaRE, a lightweight representation-level technique to identify where these ripple effects may occur. Unlike prior gradient-based methods, CLaRE quantifies entanglement between facts using forward activations from a single intermediate layer, avoiding costly backward passes. To enable systematic study, we prepare and analyse a corpus of 11,427 facts drawn from three existing datasets. Using CLaRE, we compute large-scale entanglement graphs of this corpus for multiple models, capturing how local edits propagate through representational space. These graphs enable stronger preservation sets for model editing, audit trails, efficient red-teaming, and scalable post-edit evaluation. In comparison to baselines, CLaRE achieves an average of 62.2% improvement in Spearman correlation with ripple effects while being $2.74\times$ faster, and using $2.85\times$ less peak GPU memory. Besides, CLaRE requires only a fraction of the storage needed by the baselines to compute and preserve fact representations. Our entanglement graphs and corpus are available at https://anonymous.4open.science/r/CLaRE-488E.
Executive Summary
This article presents a novel technique called CLaRE (CLaRE-ty Amid Chaos) for identifying potential ripple effects in large language model (LLM) editing. By quantifying representational entanglement using forward activations from a single intermediate layer, CLaRE outperforms baseline methods in terms of accuracy and computational efficiency. The authors demonstrate the effectiveness of CLaRE on a corpus of 11,427 facts drawn from three existing datasets, achieving a 62.2% improvement in Spearman correlation with ripple effects. CLaRE's entanglement graphs also enable stronger preservation sets, audit trails, and efficient red-teaming. This technique has significant implications for LLM editing, model auditing, and post-edit evaluation, making it a valuable contribution to the field of natural language processing.
Key Points
- ▸ CLaRE is a lightweight representation-level technique for identifying ripple effects in LLM editing.
- ▸ CLaRE uses forward activations from a single intermediate layer to quantify representational entanglement.
- ▸ CLaRE outperforms baseline methods in terms of accuracy and computational efficiency.
Merits
Strength in quantifying entanglement
CLaRE's ability to quantify entanglement using forward activations from a single intermediate layer is a significant strength, as it avoids costly backward passes and reduces computational complexity.
Improved accuracy and efficiency
CLaRE achieves a 62.2% improvement in Spearman correlation with ripple effects and is $2.74 imes$ faster and uses $2.85 imes$ less peak GPU memory than baseline methods.
Flexibility and scalability
CLaRE's entanglement graphs enable stronger preservation sets, audit trails, and efficient red-teaming, making it a valuable tool for LLM editing and model auditing.
Demerits
Limited dataset size
The corpus of 11,427 facts drawn from three existing datasets may be limited in scope and size, which could impact the generalizability of CLaRE's results.
Dependence on model architecture
CLaRE's performance may be sensitive to the specific model architecture used, which could impact its applicability to different models and tasks.
Expert Commentary
The article presents a well-structured and well-argued case for CLaRE's effectiveness in identifying ripple effects in LLM editing. The authors provide a clear and concise explanation of the technique and its benefits, and the results are impressive. However, the limitations of the dataset size and dependence on model architecture should be addressed in future work. Overall, CLaRE is a valuable contribution to the field of natural language processing and has significant implications for LLM editing and model auditing.
Recommendations
- ✓ Future work should aim to expand the dataset size and scope to improve the generalizability of CLaRE's results.
- ✓ Researchers should investigate the applicability of CLaRE to different model architectures and tasks to ensure its flexibility and scalability.
Sources
Original: arXiv - cs.LG
