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Evaluating the Ripple Effects of Knowledge Editing in Language Models
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Modern language models capture a large body of factual knowledge. However, some facts can be incorrectly induced or become obsolete over time, resulting in factually incorrect generations. This has led to the development of various editing methods that allow updating facts encoded by the model. Evaluation of these methods has primarily focused on testing whether an individual fact has been successfully injected, and if similar predictions for other subjects have not changed. Here we argue that such evaluation is limited, since injecting one fact (e.g. ``Jack Depp is the son of Johnny Depp'') introduces a ``ripple effect'' in the form of additional facts that the model needs to update (e.g.``Jack Depp is the sibling of Lily-Rose Depp''). To address this issue, we propose a novel set of evaluation criteria that consider the implications of an edit on related facts. Using these criteria, we then construct RippleEdits, a diagnostic benchmark of 5K factual edits, capturing a variety of types of ripple effects. We evaluate prominent editing methods on RippleEdits, showing that current methods fail to introduce consistent changes in the model's knowledge. In addition, we find that a simple in-context editing baseline obtains the best scores on our benchmark, suggesting a promising research direction for model editing.
Forward citations
Cited by 5 Pith papers
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Towards Efficient and Effective Alignment of Large Language Models
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Towards a Principled Evaluation of Knowledge Editors
The choice of evaluation metric, generation length, and edit batch size changes the ranking of knowledge editors, and exact string matching produces false positives.
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Efficient Knowledge Editing via Minimal Precomputation
Precomputing only a few thousand hidden vectors instead of 44 million is enough for MEMIT, ROME, and EMMET editing to match full-precomputation scores on CounterFact.
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COMPKE: Complex Question Answering under Knowledge Editing
COMPKE is a new benchmark with 11,924 complex questions that tests knowledge editing through one-to-many relations and logical operations, where existing editing methods often fail.
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Break Through the Compression Bottleneck: From Theory to Practice
The paper asserts a first proof that low-rank decomposition and quantization are non-orthogonal tools for LLM compression, recommends low-rank-first ordering, and adds a diagonal scaling fix (DAM) that reduces the com...
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