63 citations · 79 across the 19 of their papers we have counts for
7 papers · 1 filter
Large Language Model Safety: A Holistic Survey
Dan Shi, Tianhao Shen, Yufei Huang +10
The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural lang…
Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning
Hang Zhou, Yehui Tang, Haochen Qin +5
The efficacy of large language models (LLMs) on downstream tasks usually hinges on instruction tuning, which relies critically on the quality of training data. Unfortunately, colle…
CRiskEval: A Chinese Multi-Level Risk Evaluation Benchmark Dataset for Large Language Models
Ling Shi, Deyi Xiong
Large language models (LLMs) are possessed of numerous beneficial capabilities, yet their potential inclination harbors unpredictable risks that may materialize in the future. We h…
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…
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…
FineMath: A Fine-Grained Mathematical Evaluation Benchmark for Chinese Large Language Models
Yan Liu, Renren Jin, Ling Shi +2
To thoroughly assess the mathematical reasoning abilities of Large Language Models (LLMs), we need to carefully curate evaluation datasets covering diverse mathematical concepts an…
A Comprehensive Evaluation of Quantization Strategies for Large Language Models
Renren Jin, Jiangcun Du, Wuwei Huang +4
Increasing the number of parameters in large language models (LLMs) usually improves performance in downstream tasks but raises compute and memory costs, making deployment difficul…