Large language models (LLMs) have demonstrated impressive reasoning abilities in
complex tasks. However, they lack up-to-date knowledge and experience hallucinations …

Answering complex factual questions has drawn a lot of attention. Researchers leverage
various data sources to support complex QA, such as unstructured texts, structured …

Knowledge Base Question Answering (KBQA) aims to answer natural language questions
over large-scale knowledge bases (KBs), which can be summarized into two crucial steps …

Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in
addressing the challenges of Large Language Models (LLMs) without necessitating …

Complex Question Answering over Knowledge Graph (C-KGQA) seeks to solve complex
questions using knowledge graphs. Currently, KGQA systems achieve great success in …

The ability to generalise well is one of the primary desiderata of natural language
processing (NLP). Yet, what'good generalisation'entails and how it should be evaluated is …

Pre-trained language models (PLMs) have shown their effectiveness in multiple scenarios.
However, KBQA remains challenging, especially regarding coverage and generalization …

Abstract Knowledge base question answering (KBQA) is a critical yet challenging task due
to the vast number of entities within knowledge bases and the diversity of natural language …

Knowledge base question answering (KBQA) aims to answer a question over a knowledge
base (KB). Early studies mainly focused on answering simple questions over KBs and …

Question answering over knowledge bases (KBs) aims to answer natural language
questions with factual information such as entities and relations in KBs. Previous methods …