Multi Hop QA

Multi-hop question answering (MHQA) focuses on developing systems capable of answering complex questions requiring the integration of information from multiple sources, mimicking human reasoning processes. Current research emphasizes improving the accuracy and efficiency of these systems, exploring techniques like retrieval-augmented generation, multi-agent flows, and graph neural networks to better handle complex reasoning paths and overcome limitations of large language models (LLMs). A key challenge is evaluating model performance objectively, leading to the development of new benchmarks designed to mitigate data contamination and assess the quality of the reasoning chain itself. Advances in MHQA have significant implications for improving the reliability and interpretability of AI systems in various knowledge-intensive applications.