Xiezhi: An Ever-Updating Benchmark for Holistic Domain Knowledge Evaluation
This benchmark addresses the need for updated NLP evaluation tools to assess LLMs' domain knowledge across diverse fields, though it is incremental as it builds on existing benchmarking approaches.
The authors introduced Xiezhi, a comprehensive benchmark with 249,587 multiple-choice questions across 516 disciplines, to evaluate holistic domain knowledge in large language models (LLMs). Results showed that LLMs outperformed humans in science, engineering, agronomy, medicine, and art but underperformed in economics, jurisprudence, pedagogy, literature, history, and management.
New Natural Langauge Process~(NLP) benchmarks are urgently needed to align with the rapid development of large language models (LLMs). We present Xiezhi, the most comprehensive evaluation suite designed to assess holistic domain knowledge. Xiezhi comprises multiple-choice questions across 516 diverse disciplines ranging from 13 different subjects with 249,587 questions and accompanied by Xiezhi-Specialty and Xiezhi-Interdiscipline, both with 15k questions. We conduct evaluation of the 47 cutting-edge LLMs on Xiezhi. Results indicate that LLMs exceed average performance of humans in science, engineering, agronomy, medicine, and art, but fall short in economics, jurisprudence, pedagogy, literature, history, and management. We anticipate Xiezhi will help analyze important strengths and shortcomings of LLMs, and the benchmark is released in~\url{https://github.com/MikeGu721/XiezhiBenchmark}.