WebNovelBench: Placing LLM Novelists on the Web Novel Distribution
Authors: name: Leon Lin, name: Jun Zheng, name: Haidong Wang

Robustly evaluating the long-form storytelling capabilities of Large Language
Models (LLMs) remains a significant challenge, as existing benchmarks often
lack the necessary scale, diversity, or objective measures. To address this, we
introduce WebNovelBench, a novel benchmark specifically designed for evaluating
long-form novel generation. WebNovelBench leverages a large-scale dataset of
over 4,000 Chinese web novels, framing evaluation as a synopsis-to-story
generation task. We propose a multi-faceted framework encompassing eight
narrative quality dimensions, assessed automatically via an LLM-as-Judge
approach. Scores are aggregated using Principal Component Analysis and mapped
to a percentile rank against human-authored works. Our experiments demonstrate
that WebNovelBench effectively differentiates between human-written
masterpieces, popular web novels, and LLM-generated content. We provide a
comprehensive analysis of 24 state-of-the-art LLMs, ranking their storytelling
abilities and offering insights for future development. This benchmark provides
a scalable, replicable, and data-driven methodology for assessing and advancing
LLM-driven narrative generation.