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

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

Answering open-domain questions requires world knowledge about in-context entities. As
pre-trained Language Models (LMs) lack the power to store all required knowledge, external …

Large language models have achieved outstanding performance on various downstream
tasks with their advanced understanding of natural language and zero-shot capability …

Abstract Knowledge Graph Question Answering (KGQA) aims to answer user-questions from
a knowledge graph (KG) by identifying the reasoning relations between topic entity and …

Abstract Knowledge Base Question Answering (KBQA) task is an important research
direction in natural language processing. Due to the flexibility and ambiguity of natural …

We present UniBind a flexible and efficient approach that learns a unified representation
space for seven diverse modalities--images text audio point cloud thermal video and event …

Question answering over knowledge graph (KGQA) aims at answering natural language
questions posed over knowledge graphs (KGs). Moreover, multi-hop KGQA requires …

Large language models (LLMs) are widely adopted in knowledge-intensive tasks and have
achieved impressive performance thanks to their knowledge abilities. While LLMs have …

Knowledge Base Question Answering (KBQA) aims to answer natural language questions
with factual information such as entities and relations in KBs. However, traditional Pre …