Textual understanding boost in the WikiRace

2025-11-13

AI Summary

This work investigates WikiRace—a zero-shot navigation task on the Wikipedia hyperlink graph from a source to a target article—as a benchmark for semantic-driven search in complex information networks. We propose a lightweight semantic navigation method that computes semantic similarity solely via language-model-generated article title embeddings, employs greedy forward search with a simple cycle-avoidance mechanism, and deliberately omits structural graph features or explicit path planning. Experiments on large-scale Wikipedia subgraphs achieve 100% success rate, with navigation efficiency one order of magnitude higher than state-of-the-art structured or hybrid approaches. Our core contribution is the first systematic demonstration that pure semantic similarity—without fine-tuning, external knowledge, or graph algorithms—is sufficient for robust zero-shot navigation in complex networks. This reveals the intrinsic potential of large language models as universal semantic navigators.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

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.

Problem

• Evaluating navigation strategies for Wikipedia article link navigation
• Comparing structural, semantic and hybrid approaches for network search
• Assessing language models as semantic navigators in complex information spaces

Innovation

• Semantic similarity guides greedy navigation strategy
• Combines loop-avoidance with semantic title matching
• Uses language models as zero-shot semantic navigators