Human Reasoning
Human reasoning research investigates how humans and artificial intelligence (AI) systems draw inferences and solve problems, aiming to understand and replicate human-like cognitive processes. Current research focuses on enhancing AI reasoning capabilities, particularly in large language models (LLMs), through techniques like chain-of-thought prompting, multi-model collaboration, and the integration of world and agent models. This work is significant because it addresses limitations in current AI systems and has implications for improving AI decision-making in various applications, including autonomous driving and claim verification, while also providing insights into the nature of human cognition itself.
Papers
Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning
Debjit Paul, Robert West, Antoine Bosselut, Boi Faltings
Hybrid Reasoning Based on Large Language Models for Autonomous Car Driving
Mehdi Azarafza, Mojtaba Nayyeri, Charles Steinmetz, Steffen Staab, Achim Rettberg