EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning

TL;DR

EconLogicQA is a new benchmark designed to evaluate large language models' ability to perform complex sequential reasoning in economics, business, and supply chain scenarios, emphasizing understanding interconnected events.

Contribution

We introduce EconLogicQA, a challenging benchmark that assesses LLMs' capacity for multi-event reasoning in economic contexts, filling a gap in existing evaluation methods.

Findings

EconLogicQA effectively measures LLMs' sequential reasoning in economics.

Leading LLMs show varying proficiency on the benchmark.

The dataset is publicly available for further research.

Abstract

In this paper, we introduce EconLogicQA, a rigorous benchmark designed to assess the sequential reasoning capabilities of large language models (LLMs) within the intricate realms of economics, business, and supply chain management. Diverging from traditional benchmarks that predict subsequent events individually, EconLogicQA poses a more challenging task: it requires models to discern and sequence multiple interconnected events, capturing the complexity of economic logics. EconLogicQA comprises an array of multi-event scenarios derived from economic articles, which necessitate an insightful understanding of both temporal and logical event relationships. Through comprehensive evaluations, we exhibit that EconLogicQA effectively gauges a LLM's proficiency in navigating the sequential complexities inherent in economic contexts. We provide a detailed description of EconLogicQA dataset and shows the outcomes from evaluating the benchmark across various leading-edge LLMs, thereby offering a thorough perspective on their sequential reasoning potential in economic contexts. Our benchmark dataset is available at https://huggingface.co/datasets/yinzhu-quan/econ_logic_qa.