QAsystem

QAsystem is a software system designed to answer questions posed in natural language. It combines natural language understanding, information retrieval, and answer generation to provide concise responses. It is used in search engines, virtual assistants, and customer-support tools, and can operate in open-domain or restricted-domain settings.

Its core architecture typically includes a question processor, a retrieval component, a reasoning or inference module, and an answer generator or selector. Some designs also incorporate a dialogue manager to handle clarifications and follow-up questions, and a knowledge base to store structured information.

Approaches vary from rule-based and symbolic methods to statistical models and neural networks. Modern systems often employ retrieval-augmented generation or end-to-end transformers that rank candidates or generate grounded answers from documents or knowledge graphs. QA tasks are commonly categorized as factoid or open-domain, reading-comprehension, or multi-hop reasoning.

Data sources include unstructured text (web pages, documents), structured data (knowledge graphs, databases), and domain-specific corpora. Evaluation uses metrics such as exact match and F1 for factoid questions, with additional measures for answer quality and safety. Benchmarks like SQuAD, Natural Questions, and TriviaQA are widely used in research.

Key challenges include ambiguity and incomplete information, complex multi-step reasoning, model hallucination, bias, and multilingual coverage. Research focuses on grounding answers in reliable sources, improving interpretability and efficiency, reducing hallucinations, and integrating retrieval and generation more closely.