Are LLMs Capable of Data-based Statistical and Causal Reasoning?
  Benchmarking Advanced Quantitative Reasoning with Data
Xiao Liu Zirui Wu Xueqing Wu Pan Lu Kai-Wei Chang Yansong Feng
Quantitative reasoning is a critical skill to analyze data, yet the
assessment of such ability remains limited. To address this gap, we introduce
the Quantitative Reasoning with Data (QRData) benchmark, aiming to evaluate
Large Language Models' capability in statistical and causal reasoning with
real-world data. The benchmark comprises a carefully constructed dataset of 411
questions accompanied by data sheets from textbooks, online learning materials,
and academic papers. To compare models' quantitative reasoning abilities on
data and text, we enrich the benchmark with an auxiliary set of 290 text-only
questions, namely QRText. We evaluate natural language reasoning, program-based
reasoning, and agent reasoning methods including Chain-of-Thought,
Program-of-Thoughts, ReAct, and code interpreter assistants on diverse models.
The strongest model GPT-4 achieves an accuracy of 58%, which has a large room
for improvement. Among open-source models, Deepseek-coder-instruct, a code LLM
pretrained on 2T tokens, gets the highest accuracy of 37%. Analysis reveals
that models encounter difficulties in data analysis and causal reasoning, and
struggle in using causal knowledge and provided data simultaneously. Code and
data are in https://github.com/xxxiaol/QRData.