Evaluating the open-ended text generation of large language models (LLMs) is challenging because of the lack of a clear ground truth and the high cost of human or LLM-based assessments. We propose a novel benchmark that evaluates LLMs using n-gram statistics and rules, without relying on human judgement or LLM-as-a-judge approaches. Using 50 question and reference answer sets, we introduce three new metrics based on n-grams and rules: Fluency, Truthfulness, and Helpfulness. Our benchmark strongly correlates with GPT-4o-based evaluations while requiring significantly fewer computational resources, demonstrating its effectiveness as a scalable alternative for assessing LLMs' open-ended generation capabilities.
@article{arxiv.2502.09316,
title = {A Judge-free LLM Open-ended Generation Benchmark Based on the Distributional Hypothesis},
author = {Kentaro Imajo and Masanori Hirano and Shuji Suzuki and Hiroaki Mikami},
journal= {arXiv preprint arXiv:2502.09316},
year = {2025}
}