Author

Guo-Ming Ling

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#natural language process... Review Oct 2026

Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models

Benchmarks are essential for evaluating and improving large language models (LLMs). Yet many widely used benchmarks are redundant, making evaluation unnecessarily expensive. Benchmark compression lowers this cost by estimating full-benchmark scores and rankings from a partial evaluation. Research in this area has grown...

Guo-Ming Ling, Yupei Lin, Zhong-Zhan Huang et al. · 0 citations

Preprint Sep 2026

Jev in the Wild: A Data-Driven Analysis of the Jev Model's Functionality, Applications and Ecosystem

Jev is a fast, low-cost decision model that answers natural-language questions with choices, binary judgments, and scores. As its public ecosystem grows rapidly, it remains unclear how Jev is used across applications and how public attention relates to project distribution. To answer these questions, we conduct a large...

Guo-Ming Ling, Mu-En Xue, Zi-Jian Ye · 6 citations · ⚡1

#natural language process... Preprint Sep 2026

Zipbench: Low-Cost Framework for Compressing Comprehensive Benchmarks of Large Language Models

This work presents ZipBench, a simple and low-cost BCM with theoretical error and rank-consistency guarantees that reduces the cost of both LLM evaluation and compact benchmark construction, lowering the barrier to broad LLM research for compute-constrained researchers.

Zhong-Zhan Huang, Jun-Xin Li, Guo-Ming Ling et al. · 0 citations