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20232026
most citedEvaluating Large Language Models: A Comprehensive Survey

63 citations · 106 across the 6 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

DEP: A Decentralized Large Language Model Evaluation Protocol

Jianxiang Peng, Junhao Li, Hongxiang Wang +15

With the rapid development of Large Language Models (LLMs), a large number of benchmarks have been proposed. However, most benchmarks lack unified evaluation standard and require t…

cs.CL2024

LHMKE: A Large-scale Holistic Multi-subject Knowledge Evaluation Benchmark for Chinese Large Language Models

Chuang Liu, Renren Jin, Yuqi Ren +1

Chinese Large Language Models (LLMs) have recently demonstrated impressive capabilities across various NLP benchmarks and real-world applications. However, the existing benchmarks…

cs.CL2024

OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety

Chuang Liu, Linhao Yu, Jiaxuan Li +11

The rapid development of Chinese large language models (LLMs) poses big challenges for efficient LLM evaluation. While current initiatives have introduced new benchmarks or evaluat…

cs.CL2023★ 63 cited

Evaluating Large Language Models: A Comprehensive Survey

Zishan Guo, Renren Jin, Chuang Liu +8

Large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks. They have attracted significant attention and been deployed in numerous dow…

cs.CL2023★ 36 cited

Large Language Model Alignment: A Survey

Tianhao Shen, Renren Jin, Yufei Huang +6

Recent years have witnessed remarkable progress made in large language models (LLMs). Such advancements, while garnering significant attention, have concurrently elicited various c…

cs.CL2023★ 7 cited

M3KE: A Massive Multi-Level Multi-Subject Knowledge Evaluation Benchmark for Chinese Large Language Models

Chuang Liu, Renren Jin, Yuqi Ren +10

Large language models have recently made tremendous progress in a variety of aspects, e.g., cross-task generalization, instruction following. Comprehensively evaluating the capabil…