Can Knowledge Editing Really Correct Hallucinations?
Large Language Models (LLMs) suffer from hallucinations, referring to the
non-factual information in generated content, despite their superior capacities
across tasks. Meanwhile, knowledge editing has been developed as a new popular
paradigm to correct the erroneous factual knowledge encoded in LLMs with the
advantage of avoiding retraining from scratch. However, one common issue of
existing evaluation datasets for knowledge editing is that they do not ensure
LLMs actually generate hallucinated answers to the evaluation questions before
editing. When LLMs are evaluated on such datasets after being edited by
different techniques, it is hard to directly adopt the performance to assess
the effectiveness of different knowledge editing methods in correcting
hallucinations. Thus, the fundamental question remains insufficiently
validated: Can knowledge editing really correct hallucinations in LLMs? We
proposed HalluEditBench to holistically benchmark knowledge editing methods in
correcting real-world hallucinations. First, we rigorously construct a massive
hallucination dataset with 9 domains, 26 topics and more than 6,000
hallucinations. Then, we assess the performance of knowledge editing methods in
a holistic way on five dimensions including Efficacy, Generalization,
Portability, Locality, and Robustness. Through HalluEditBench, we have provided
new insights into the potentials and limitations of different knowledge editing
methods in correcting hallucinations, which could inspire future improvements
and facilitate the progress in the field of knowledge editing.
67171d2869532dd147c9d9f8
Baixiang Huang
admin_assigned
2024-10-25T11:35:51.119Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64821657564f4a586629a76a/LoBNDQFD7yHiueESY14Ic.jpeg
Baixiang Huang
user
BaixHuang
67171d2869532dd147c9d9f9
Canyu Chen
claimed_verified
2024-10-25T09:31:07.681Z
6483af58571c2dcfa98cae82
https://cdn-avatars.huggingface.co/v1/production/uploads/6483af58571c2dcfa98cae82/d8Vnh_EG1KxeEoNAAQXHZ.jpeg
Canyu Chen
user
canyuchen
67171d2869532dd147c9d9fa
Xiongxiao Xu
admin_assigned
2024-10-25T11:35:57.623Z
65da7f82ad7ccf910d246371
/avatars/d28b12d703d3bc62ac4b8ea30faae7c9.svg
Xiongxiao Xu
user
XiongxiaoXu
67171d2869532dd147c9d9fb
Ali Payani
admin_assigned
2024-10-25T11:36:04.198Z
6411f7af46e5f8976912d62a
/avatars/d8d5b012dc7704619fcd37b809b43059.svg
Ali Payani
user
apayani
67171d2869532dd147c9d9fc
Kai Shu
admin_assigned
2024-10-25T11:36:10.091Z
671774e5fc12736836da240f
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/dE_--2tAQaEP7atMh35Ed.png
Kai Shu
user
kaishu666
Can Knowledge Editing Really Correct Hallucinations?
Large Language Models (LLMs) suffer from hallucinations, referring to the
non-factual information in generated content, despite their superior capacities
across tasks. Meanwhile, knowledge editing has been developed as a new popular
paradigm to correct the erroneous factual knowledge encoded in LLMs with the
advantage of avoiding retraining from scratch. However, one common issue of
existing evaluation datasets for knowledge editing is that they do not ensure
LLMs actually generate hallucinated answers to the evaluation questions before
editing. When LLMs are evaluated on such datasets after being edited by
different techniques, it is hard to directly adopt the performance to assess
the effectiveness of different knowledge editing methods in correcting
hallucinations. Thus, the fundamental question remains insufficiently
validated: Can knowledge editing really correct hallucinations in LLMs? We
proposed HalluEditBench to holistically benchmark knowledge editing methods in
correcting real-world hallucinations. First, we rigorously construct a massive
hallucination dataset with 9 domains, 26 topics and more than 6,000
hallucinations. Then, we assess the performance of knowledge editing methods in
a holistic way on five dimensions including Efficacy, Generalization,
Portability, Locality, and Robustness. Through HalluEditBench, we have provided
new insights into the potentials and limitations of different knowledge editing
methods in correcting hallucinations, which could inspire future improvements
and facilitate the progress in the field of knowledge editing.
HalluEditBench benchmarks knowledge editing methods in correcting hallucinations in Large Language Models (LLMs) using a comprehensive dataset and evaluation framework.
Large Language Models LLMs hallucinations knowledge editing evaluation datasets holistic benchmarking