title: Evaluating the Ripple Effects of Knowledge Editing in Language Models
summary: 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.
paper_authors._id: 64bf59a98496ee0fb638ccf2
paper_authors.name: Roi Cohen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-12T08:23:09.935Z
paper_authors._id: 65b7afaeb4ec059b974e9189
paper_authors.avatarUrl: /avatars/08059754f2d299b76ab4649e8d768253.svg
paper_authors.fullname: Roi Cohen
paper_authors.type: user
paper_authors.user: roicohen9
paper_authors._id: 64bf59a98496ee0fb638ccf3
paper_authors.name: Eden Biran
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-26T08:43:52.574Z
paper_authors._id: 64c011ae227250691ab7a5af
paper_authors.avatarUrl: /avatars/fce9b1ada70a567c4746651a491bdef6.svg
paper_authors.fullname: Eden Biran
paper_authors.type: user
paper_authors.user: edenbiran
paper_authors._id: 64bf59a98496ee0fb638ccf4
paper_authors.name: Ori Yoran
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-23T07:52:32.692Z
paper_authors._id: 610b7ccff5dd7bcefedaff57
paper_authors.avatarUrl: /avatars/a1e77460105a56f84371fa02d530a71e.svg
paper_authors.fullname: Yoran
paper_authors.type: user
paper_authors.user: Ori
paper_authors._id: 64bf59a98496ee0fb638ccf5
paper_authors.name: Amir Globerson
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-25T08:20:59.580Z
paper_authors._id: 62cd956bc589e4a9e23ea347
paper_authors.avatarUrl: /avatars/dbdccd9258c9d18173d312a8bf14dd6e.svg
paper_authors.fullname: Amir Globerson
paper_authors.type: user
paper_authors.user: amirgloberson
paper_authors._id: 64bf59a98496ee0fb638ccf6
paper_authors.name: Mor Geva
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-25T08:21:20.109Z
paper_authors._id: 610b729f9da682cd54ad9adf
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1628140189042-noauth.jpeg
paper_authors.fullname: Mor Geva
paper_authors.type: user
paper_authors.user: mega
paper_title: Evaluating the Ripple Effects of Knowledge Editing in Language Models
paper_summary: 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.
paper_ai_keywords: language models factual knowledge editing methods evaluation criteria ripple effect in-context editing