用标题相似度导航维基百科，比结构方法快十倍且100%成功。
Textual understanding boost in the WikiRace
- 仅靠文章标题语义相似度做贪心导航
- 成功率100%，效率比结构方法高一个数量级
- 适合需要快速理解复杂信息的读者
WikiRace游戏通过仅使用超链接在维基百科文章间导航，为复杂信息网络中的目标导向搜索提供了一个有力基准。本文系统评估了多种导航策略，包括基于图论结构（介数中心性）、语义意义（语言模型嵌入）以及混合方法。在大规模维基百科子图上的严格测试表明，仅依赖文章标题语义相似度的贪心代理表现极为出色。该策略结合简单的环路避免机制后，实现了100%的成功率，导航效率比结构或混合方法高出一个数量级。研究揭示了纯结构启发式在目标导向搜索中的显著局限性，并凸显了大语言模型作为零样本语义导航器在复杂信息空间中的变革潜力。
原文摘要 · Abstract (English)
The WikiRace game, where players navigate between Wikipedia articles using only hyperlinks, serves as a compelling benchmark for goal-directed search in complex information networks. This paper presents a systematic evaluation of navigation strategies for this task, comparing agents guided by graph-theoretic structure (betweenness centrality), semantic meaning (language model embeddings), and hybrid approaches. Through rigorous benchmarking on a large Wikipedia subgraph, we demonstrate that a purely greedy agent guided by the semantic similarity of article titles is overwhelmingly effective. This strategy, when combined with a simple loop-avoidance mechanism, achieved a perfect success rate and navigated the network with an efficiency an order of magnitude better than structural or hybrid methods. Our findings highlight the critical limitations of purely structural heuristics for goal-directed search and underscore the transformative potential of large language models to act as powerful, zero-shot semantic navigators in complex information spaces.
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