2024/07/25 by Abdolmahdi Bagheri, Matin Alinejad, Bagheri, Abdolmahdi +6 · 1 citation
Computer Science · Decision Sciences · #Data Quality and Management #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2407.18069
openalex publication_date 2024/07/25 · openalex created_date 2024/09/10 · openalex updated_date 2026/07/28
Causal reasoning is one of the primary bottlenecks that Large Language Models (LLMs) must overcome to attain human-level intelligence. Recent studies indicate that LLMs display near-random performance on reasoning tasks. To address this, we introduce the Causal Chain of Prompting (C2P), a reasoning framework that aims to equip current LLMs with causal reasoning capabilities as the first framework of its kind operating autonomously without relying on external tools or modules during both the causal learning and reasoning phases. To evaluate the performance of C2P, we first demonstrate that reasoning accuracy improved by over 30.7% and 25.9% for GPT-4 Turbo and LLaMA 3.1, respectively, when using our framework, compared to the same models without C2P on a synthetic benchmark dataset. Then, using few-shot learning of the same LLMs with C2P, the reasoning accuracy increased by more than 20.05% and 20.89%, respectively, with as few as ten examples, compared to the corresponding LLMs without C2P on the same dataset. To evaluate C2P in realistic scenarios, we utilized another benchmark dataset containing natural stories across various fields, including healthcare, medicine, economics, education, social sciences, environmental science, and marketing. The results show improved reasoning when C2P is applied, compared to cases where our framework is not used, which often leads to random and hallucinated responses. By showing the improved performance of few-shot learned GPT-4 Turbo and LLaMA 3.1 with C2P, we demonstrate the generalizability of our framework.