WritingBench: A Comprehensive Benchmark for Generative Writing
Authors: name: Yuning Wu, name: Jiahao Mei, name: Ming Yan, name: Chenliang Li, name: SHaopeng Lai, name: Yuran Ren, name: Zijia Wang, name: Ji Zhang, name: Mengyue Wu, name: Qin Jin, name: Fei Huang

Recent advancements in large language models (LLMs) have significantly
enhanced text generation capabilities, yet evaluating their performance in
generative writing remains a challenge. Existing benchmarks primarily focus on
generic text generation or limited in writing tasks, failing to capture the
diverse requirements of high-quality written contents across various domains.
To bridge this gap, we present WritingBench, a comprehensive benchmark designed
to evaluate LLMs across 6 core writing domains and 100 subdomains, encompassing
creative, persuasive, informative, and technical writing. We further propose a
query-dependent evaluation framework that empowers LLMs to dynamically generate
instance-specific assessment criteria. This framework is complemented by a
fine-tuned critic model for criteria-aware scoring, enabling evaluations in
style, format and length. The framework's validity is further demonstrated by
its data curation capability, which enables 7B-parameter models to approach
state-of-the-art (SOTA) performance. We open-source the benchmark, along with
evaluation tools and modular framework components, to advance the development
of LLMs in writing.