Popular Knowledge Propagates More Errors in LLM Knowledge Updating
Updating a language model's knowledge through fine-tuning is essential for keeping its outputs current, yet can also induce factual forgetting and new hallucinations. Prior work shows that long-tail knowledge is harder to acquire and newly memorized long-tail facts are difficult to retain during later fine-tuning. We study a complementary question: among facts that a model has encoded correctly, which are most vulnerable to collateral corruption during other updates? To investigate this question under a realistic factual distribution, we construct a large-scale graph FACTPROP of verified Wikipedia facts by linking triples that share head or tail entities, thereby preserving connections am...
| Authors | Yuji Zhang, Weibing Wang, Cheng Qian et al. |
|---|---|
| Primary category | cs.CL |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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