The key contribution here is a rigorous real-world evaluation of model editing methods, specifically introducing QAEdit - a new benchmark that tests editing effectiveness without the artificial advantages of teacher forcing during evaluation.
Main technical points: - Current editing methods show 38.5% success rate in realistic conditions vs. 96% reported with teacher forcing - Sequential editing performance degrades significantly after ~1000 edits - Teacher forcing during evaluation creates artificially high results by providing ground truth tokens - QAEdit benchmark derived from established QA datasets (SQuAD, TriviaQA, NQ) - Tested across multiple model architectures and editing methods
The methodology reveals several critical findings: - Previous evaluations used teacher forcing during testing, which doesn't reflect real deployment - Models struggle to maintain consistency across related questions - Performance varies significantly between different types of factual edits - Larger models don't necessarily show better editing capabilities
I think this work fundamentally changes how we need to approach model editing research. The dramatic drop in performance from lab to realistic conditions (96% to 38.5%) suggests we need to completely rethink our evaluation methods. The sequential editing results also raise important questions about the practical scalability of current editing approaches.
I think the QAEdit benchmark could become a standard tool for evaluating editing methods, similar to how GLUE became standard for language understanding tasks. The results suggest that making model editing practical will require significant methodological advances beyond current approaches.
TLDR: Current model editing methods perform far worse than previously reported (38.5% vs 96% success rate) when evaluated in realistic conditions. Sequential editing fails after ~1000 edits. New QAEdit benchmark proposed for more rigorous evaluation.
Full summary is here. Paper here.
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