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

13 May 2024arXiv:2405.07938archive 2025-07-28

Yinzhu Quan, Zefang Liu

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.

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Tasks

ArticlesManagementMultiple Choice Question Answering (MCQA)Question AnsweringSentence Ordering

Datasets

Introduced by this paper, per the archive.

EconLogicQA

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Sentence Ordering EconLogicQA GPT-4-Turbo Accuracy 0.5692 #1 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA GPT-4 Accuracy 0.5538 #2 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA GPT-3.5-Turbo Accuracy 0.3769 #3 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Llama-3-8B-Instruct Accuracy 0.3462 #4 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Mistral-7B-Instruct-v0.2 Accuracy 0.3154 #5 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Mistral-7B-v0.1 Accuracy 0.2615 #6 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Mistral-7B-v0.2 Accuracy 0.2615 #7 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Llama-3-8B Accuracy 0.2385 #8 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Zephyr-7B-Alpha Accuracy 0.2308 #9 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Yi-6B-Chat Accuracy 0.2077 #10 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Zephyr-7B-Beta Accuracy 0.1769 #11 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Mistral-7B-Instruct-v0.1 Accuracy 0.1538 #12 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Llama-2-13B-Chat Accuracy 0.1462 #13 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Llama-2-7B-Chat Accuracy 0.0923 #14 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Gemma-2B-IT Accuracy 0.0846 #15 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Yi-6B Accuracy 0.0385 #16 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Gemma-7B-IT Accuracy 0.0231 #17 of 18 Archive leaderboard report
Sentence Ordering EconLogicQA Llama-2-7B Accuracy 0.0077 #18 of 18 Archive leaderboard report

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