Natural Language Question

Natural language question answering (NLQA) focuses on enabling computers to understand and respond to questions posed in human language, leveraging structured data sources like knowledge graphs and databases or unstructured text corpora. Current research emphasizes improving the accuracy and efficiency of NLQA systems, particularly for complex questions requiring multi-step reasoning, by employing techniques like question-guided knowledge graph rescoring, hybrid question parsing and execution, and the integration of large language models (LLMs) with symbolic methods. These advancements are crucial for unlocking the vast potential of data stored in various formats, facilitating easier access to information and enabling more sophisticated applications across diverse fields, including biomedical research and software development.