CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing

A team of researchers has introduced CLaRE (Clarity Amid Chaos), a novel technique that quantifies representational entanglement to predict the unpredictable ripple effects caused by editing large language models. When developers modify a single factual association within an LLM—like correcting a CEO's name or a historical date—the change can inadvertently alter the model's behavior on seemingly unrelated topics. CLaRE addresses this by creating large-scale "entanglement graphs" that map how over 11,000 facts are interconnected within a model's hidden representations, using only forward activations from a single intermediate layer. This approach avoids the computational cost of traditional gradient-based methods.

CLaRE's performance is significant: it achieves a 62.2% average improvement in Spearman correlation with actual ripple effects compared to baselines, while being 2.74 times faster and using 2.85 times less peak GPU memory. The method also requires far less storage to compute and preserve fact representations. The researchers applied CLaRE to a corpus of 11,427 facts drawn from three existing datasets, analyzing multiple models to systematically study how local edits propagate. The resulting graphs are publicly available and serve as powerful tools for creating stronger preservation sets during editing, establishing audit trails, enabling efficient red-teaming, and scaling post-edit evaluation workflows.