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
A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning
Ye Yuan, Chengwu Liu, Jingyang Yuan, Gongbo Sun, Siqi Li, Ming Zhang
Order Matters in Hallucination: Reasoning Order as Benchmark and Reflexive Prompting for Large-Language-Models
Zikai Xie
Towards a Generative Approach for Emotion Detection and Reasoning
Ankita Bhaumik, Tomek Strzalkowski
On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey
Jingcai Guo, Zhijie Rao, Zhi Chen, Song Guo, Jingren Zhou, Dacheng Tao