@inproceedings{huang-etal-2025-minilongbench,
title = "{M}ini{L}ong{B}ench: The Low-cost Long Context Understanding Benchmark for Large Language Models",
author = "Huang, Zhongzhan and
Ling, Guoming and
Zhong, Shanshan and
Wu, Hefeng and
Lin, Liang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.560/",
doi = "10.18653/v1/2025.acl-long.560",
pages = "11442--11460",
ISBN = "979-8-89176-251-0",
abstract = "Long Context Understanding (LCU) is a critical area for exploration in current large language models (LLMs). However, due to the inherently lengthy nature of long-text data, existing LCU benchmarks for LLMs often result in prohibitively high evaluation costs, like testing time and inference expenses. Through extensive experimentation, we discover that existing LCU benchmarks exhibit significant redundancy, which means the inefficiency in evaluation. In this paper, we propose a concise data compression method tailored for long-text data with sparse information characteristics. By pruning the well-known LCU benchmark LongBench, we create MiniLongBench. This benchmark includes only 237 test samples across six major task categories and 21 distinct tasks. Through empirical analysis of over 60 LLMs, MiniLongBench achieves an average evaluation cost reduced to only 4.5{\%} of the original while maintaining an average rank correlation coefficient of 0.97 with LongBench results. Therefore, our MiniLongBench, as a low-cost benchmark, holds great potential to substantially drive future research into the LCU capabilities of LLMs."
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%0 Conference Proceedings
%T MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models
%A Huang, Zhongzhan
%A Ling, Guoming
%A Zhong, Shanshan
%A Wu, Hefeng
%A Lin, Liang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F huang-etal-2025-minilongbench
%X Long Context Understanding (LCU) is a critical area for exploration in current large language models (LLMs). However, due to the inherently lengthy nature of long-text data, existing LCU benchmarks for LLMs often result in prohibitively high evaluation costs, like testing time and inference expenses. Through extensive experimentation, we discover that existing LCU benchmarks exhibit significant redundancy, which means the inefficiency in evaluation. In this paper, we propose a concise data compression method tailored for long-text data with sparse information characteristics. By pruning the well-known LCU benchmark LongBench, we create MiniLongBench. This benchmark includes only 237 test samples across six major task categories and 21 distinct tasks. Through empirical analysis of over 60 LLMs, MiniLongBench achieves an average evaluation cost reduced to only 4.5% of the original while maintaining an average rank correlation coefficient of 0.97 with LongBench results. Therefore, our MiniLongBench, as a low-cost benchmark, holds great potential to substantially drive future research into the LCU capabilities of LLMs.
%R 10.18653/v1/2025.acl-long.560
%U https://aclanthology.org/2025.acl-long.560/
%U https://doi.org/10.18653/v1/2025.acl-long.560
%P 11442-11460
Markdown (Informal)
[MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models](https://aclanthology.org/2025.acl-long.560/) (Huang et al., ACL 2025)
ACL