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

Question answering over knowledge bases is considered a difficult problem due to the
challenge of generalizing to a wide variety of possible natural language questions …

Existing KBQA approaches, despite achieving strong performance on iid test data, often
struggle in generalizing to questions involving unseen KB schema items. Prior ranking …

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 …

Question answering on knowledge bases (KBQA) poses a unique challenge for semantic
parsing research due to two intertwined challenges: large search space and ambiguities in …

The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …

Reasoning over natural language is a long-standing goal for the research community.
However, studies have shown that existing language models are inadequate in reasoning …