activity
20232026
most citedBenchmarking Benchmark Leakage in Large Language Models

6 citations · 20 across the 12 of their papers we have counts for

collaborators
Showing 2024Show all

5 papers · 1 filter

cs.AI2024★ 1 cited

PC Agent: While You Sleep, AI Works -- A Cognitive Journey into Digital World

Yanheng He, Jiahe Jin, Shijie Xia +5

Imagine a world where AI can handle your work while you sleep - organizing your research materials, drafting a report, or creating a presentation you need for tomorrow. However, wh…

cs.CL2024

Data Contamination Report from the 2024 CONDA Shared Task

Oscar Sainz, Iker García-Ferrero, Alon Jacovi +25

The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as…

cs.CL2024★ 2 cited

OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI

Zhen Huang, Zengzhi Wang, Shijie Xia +25

The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showc…

cs.CL2024★ 6 cited

Benchmarking Benchmark Leakage in Large Language Models

Ruijie Xu, Zengzhi Wang, Run-Ze Fan +1

Amid the expanding use of pre-training data, the phenomenon of benchmark dataset leakage has become increasingly prominent, exacerbated by opaque training processes and the often u…

cs.CL2024

Reformatted Alignment

Run-Ze Fan, Xuefeng Li, Haoyang Zou +5

The quality of finetuning data is crucial for aligning large language models (LLMs) with human values. Current methods to improve data quality are either labor-intensive or prone t…