Complex Reasoning
Complex reasoning in artificial intelligence focuses on developing models capable of multi-step, logical inference and problem-solving, mirroring human cognitive abilities. Current research emphasizes improving large language models (LLMs) through techniques like chain-of-thought prompting, retrieval-augmented generation (RAG), and the integration of symbolic reasoning with neural networks, often incorporating multi-modal data (e.g., visual and textual information). These advancements are significant for enhancing the reliability and applicability of AI systems across diverse fields, including autonomous driving, robotics, and scientific discovery, by enabling more robust and accurate decision-making in complex scenarios.
Papers
From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AI
Stefanie Krause, Frieder Stolzenburg
STOC-TOT: Stochastic Tree-of-Thought with Constrained Decoding for Complex Reasoning in Multi-Hop Question Answering
Zhenyu Bi, Daniel Hajialigol, Zhongkai Sun, Jie Hao, Xuan Wang
Reasoning in Large Language Models: A Geometric Perspective
Romain Cosentino, Sarath Shekkizhar
RVISA: Reasoning and Verification for Implicit Sentiment Analysis
Wenna Lai, Haoran Xie, Guandong Xu, Qing Li
VSP: Assessing the dual challenges of perception and reasoning in spatial planning tasks for VLMs
Qiucheng Wu, Handong Zhao, Michael Saxon, Trung Bui, William Yang Wang, Yang Zhang, Shiyu Chang