This episode analyzes the research paper titled **"Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting,"** authored by Thilini Wijesiriwardene, Ruwan Wickramarachchi, Sreeram Vennam, Vinija Jain, Aman Chadha, Amitava Das, Ponnurangam Kumaraguru, and Amit Sheth from institutions including the AI Institute at the University of South Carolina, IIIT Hyderabad, Amazon GenAI, Meta, and Stanford University. The study examines the effectiveness of nine contemporary large language models in solving proportional analogies using a newly developed dataset of 15,000 multiple-choice questions. It evaluates various knowledge-enhanced prompting techniques—exemplar, structured, and targeted knowledge—and finds that targeted knowledge significantly improves model performance, while structured knowledge does not consistently yield benefits. The research highlights ongoing challenges in the ability of large language models to process complex relational information and suggests avenues for future advancements in model training and prompting strategies.

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For more information on content and research relating to this episode please see: https://arxiv.org/pdf/2412.00869v1