Evaluating the Ripple Effects of Knowledge Editing in Language Models
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 , a
diagnostic benchmark of 5K factual edits, capturing a variety of types of
ripple effects. We evaluate prominent editing methods on , 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.
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Roi Cohen
claimed_verified
2024-12-12T08:23:09.935Z
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Roi Cohen
user
roicohen9
64bf59a98496ee0fb638ccf3
Eden Biran
claimed_verified
2023-07-26T08:43:52.574Z
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Eden Biran
user
edenbiran
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Ori Yoran
claimed_verified
2024-07-23T07:52:32.692Z
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Yoran
user
Ori
64bf59a98496ee0fb638ccf5
Amir Globerson
admin_assigned
2023-07-25T08:20:59.580Z
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Amir Globerson
user
amirgloberson
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Mor Geva
admin_assigned
2023-07-25T08:21:20.109Z
610b729f9da682cd54ad9adf
https://cdn-avatars.huggingface.co/v1/production/uploads/1628140189042-noauth.jpeg
Mor Geva
user
mega
Evaluating the Ripple Effects of Knowledge Editing in Language Models
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 , a
diagnostic benchmark of 5K factual edits, capturing a variety of types of
ripple effects. We evaluate prominent editing methods on , 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.
A new diagnostic benchmark evaluates fact editing methods' impact on related facts, revealing inconsistency and the effectiveness of in-context editing.
factual knowledge factually incorrect generations editing methods ripple effect diagnostic benchmark related facts in-context editing