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

TL;DR

Through comprehensive evaluations, this paper exhibits that EconLogicQA effectively gauges a LLM's proficiency in navigating the sequential complexities inherent in economic contexts, thereby offering a thorough perspective on their sequential reasoning potential in economic contexts.

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.

Figures (2)

• Figure 1: The prompt structure for generating sorting questions in EconLogicQA. It involves filling the contents of news articles into prompt templates. We also give an example of GPT-4 response to a specific prompt constructed from this information.
• Figure 2: Error types of GPT-4 responses on the testing set.