CLaRE quantifies representational entanglement in LLMs using single-layer forward activations to predict editing ripple effects, reporting 62.2% higher Spearman correlation than baselines while using 2.74x less time and 2.85x less GPU memory.
InProceed- ings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, pages 4907– 4926, Miami, Florida, USA
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CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing
CLaRE quantifies representational entanglement in LLMs using single-layer forward activations to predict editing ripple effects, reporting 62.2% higher Spearman correlation than baselines while using 2.74x less time and 2.85x less GPU memory.