Popular Knowledge Propagates More Errors in LLM Knowledge Updating

2026-09-08

AI Summary

研究通过构建FACTPROP图和细调模型，发现高连接度实体相关的事实更易在更新时被破坏，并提出基于流行度的锚定方法减少遗忘。

Abstract

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 among factual knowledge. We fine-tune models on factual statements and measure correct-to-incorrect facts after each update. Our results reveal a pattern distinct from prior findings on long-tail vulnerability during acquisition and retention: among facts that models already answer correctly, those associated with highly connected entities are more likely to be corrupted by neighboring updates, and updates to such facts propagate errors more broadly. Structural popularity therefore predicts both vulnerability and downstream damage. Inspired by this finding, we propose Popularity-based Anchoring (PopAnchor), a lightweight rehearsal strategy that preserves a small set of popular facts and reduces forgetting.

Problem

• knowledge updating
• factual forgetting
• hallucinations
• vulnerability
• structural popularity

Innovation

• Popularity-based Anchoring
• Factual Forgetting
• Knowledge Updating