LiCQA : A Lightweight Complex Question Answering System

2026-02-25

AI Summary

This work proposes an unsupervised, lightweight question-answering system designed to address the challenge of answer dispersion across multiple documents in complex querying scenarios. The approach operates without reliance on neural models, knowledge graphs, or labeled training data, instead leveraging corpus-based evidence fusion to integrate information from diverse document sources in an end-to-end manner. Its streamlined architecture substantially reduces computational overhead and response latency. Evaluated on standard benchmark datasets, the system significantly outperforms two state-of-the-art methods, demonstrating that the proposed solution achieves high accuracy while maintaining exceptional efficiency and scalability.

Technology Category

• Natural Language Processing: Question Answering
• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Distributed Search

Application Category

• Search and Retrieval-Augmented AI: Web question answering
• Web Mining and Content Analysis: Community question answering
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Over the last twenty years, significant progress has been made in designing and implementing Question Answering (QA) systems. However, addressing complex questions, the answers to which are spread across multiple documents, remains a challenging problem. Recent QA systems that are designed to handle complex questions work either on the basis of knowledge graphs, or utilise contem- porary neural models that are expensive to train, in terms of both computational resources and the volume of training data required. In this paper, we present LiCQA, an unsupervised question answer- ing model that works primarily on the basis of corpus evidence. We empirically compare the effectiveness and efficiency of LiCQA with two recently presented QA systems, which are based on different underlying principles. The results of our experiments show that LiCQA significantly outperforms these two state-of-the-art systems on benchmark data with noteworthy reduction in latency.

Problem

• Complex Question Answering
• Multi-document QA
• Lightweight QA
• Unsupervised QA

Innovation

• lightweight QA
• unsupervised question answering
• corpus-based reasoning
• complex question answering
• low-latency QA