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Recent model editing techniques promise to mitigate the problem of memorizingfalse or outdated associations during LLM training. However, we show that thesetechniques can introduce large unwanted side effects which are not detected byexisting specificity benchmarks. We extend the existing CounterFact benchmarkto include a dynamic component and dub our benchmark CounterFact+.Additionally, we extend the metrics used for measuring specificity by aprincipled KL divergence-based metric. We use this improved benchmark toevaluate recent model editing techniques and find that they suffer from lowspecificity. Our findings highlight the need for improved specificitybenchmarks that identify and prevent unwanted side effects.

Detecting Edit Failures In Large Language Models: An Improved Specificity Benchmark