| Challenge: | a recent study has questioned the use of direct model editing for factual corrections in LLMs. aaron s. de stefano, a sociologist, says that model editing is not a systematic remedy for factuality. |
| Approach: | They argue that direct model editing cannot be trusted as a remedy for LLM disadvantages . authors call for cautious promotion and application of model editing as part of LLM deployment process . |
| Outcome: | The proposed method is not trusted as a remedy for the disadvantages inherent to LLMs, the authors argue . they argue that it opens risks by reinforcing the notion that models can be trusted for factuality . |
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Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, Ningyu Zhang
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| Challenge: | Knowledge editing is a promising technique for updating factual knowledge in large language models (LLMs) but studies have identified side effects such as knowledge distortion and the deterioration of general abilities that have emerged after editing. |
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| Challenge: | Existing methods for factual opinion editing focus on atomic facts, ignoring the risks associated with factual opinions. |
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| Challenge: | Large language models embed extensive knowledge and perform exceptionally well across tasks. outdated knowledge or factual errors within LLMs can lead to misleading or incorrect responses. |
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| Challenge: | Existing studies have exhibited impressive success and significant potential. |
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| Challenge: | Recent model editing techniques can introduce large unwanted side effects, a new study shows . existing specificity benchmarks do not detect these unwanted side-effects . a recent study shows that model edits can cause significant performance drop . |
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Model Editing at Scale leads to Gradual and Catastrophic Forgetting (2024.findings-acl)
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| Challenge: | Existing model editing methods are evaluated using metrics for reliability, specificity and generalization over one or few edits. |
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| Challenge: | Existing models only output short phrases or sentences, raising doubts about their practical usability. |
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| Challenge: | Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment. |
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