Core Concepts
LLMs performance evaluated using Xiezhi benchmark.
Abstract
Introduction to Xiezhi evaluation suite.
Importance of benchmarks for LLMs.
Criteria for effective evaluation benchmarks.
Construction of Xiezhi dataset.
Auto-updating method for question generation and annotation.
Experiments on 47 LLMs across different benchmarks.
Results show LLMs outperform humans in certain domains but fall short in others.
Stats
Cutting-edge LLMs exceed human performance in science, engineering, agronomy, medicine, and art.
LLMs struggle in economics, jurisprudence, pedagogy, literature, history, and management.
Quotes
"New NLP benchmarks are urgently needed to align with the rapid development of large language models."
"Recent advancements in Large Language Models have shown remarkable capabilities in domain text understanding."