Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Dong, Junnan"'
Large language models (LLMs) have demonstrated their remarkable performance across various language understanding tasks. While emerging benchmarks have been proposed to evaluate LLMs in various domains such as mathematics and computer science, they m
Externí odkaz:
http://arxiv.org/abs/2410.17558
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. Existing methods can be categorized into symbolic and neural models. Symbolic models, while precise, struggle with substructure heterogeneity and s
Externí odkaz:
http://arxiv.org/abs/2410.04153
Graph anomaly detection (GAD) has been widely applied in many areas, e.g., fraud detection in finance and robot accounts in social networks. Existing methods are dedicated to identifying the outlier nodes that deviate from normal ones. While they hea
Externí odkaz:
http://arxiv.org/abs/2407.05934
Autor:
Hong, Zijin, Yuan, Zheng, Zhang, Qinggang, Chen, Hao, Dong, Junnan, Huang, Feiran, Huang, Xiao
Generating accurate SQL from natural language questions (text-to-SQL) is a long-standing challenge due to the complexities in user question understanding, database schema comprehension, and SQL generation. Conventional text-to-SQL systems, comprising
Externí odkaz:
http://arxiv.org/abs/2406.08426
Inductive knowledge graph completion (KGC) aims to infer the missing relation for a set of newly-coming entities that never appeared in the training set. Such a setting is more in line with reality, as real-world KGs are constantly evolving and intro
Externí odkaz:
http://arxiv.org/abs/2406.01140
Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their costs are significantly higher and absence of domain-specific knowledg
Externí odkaz:
http://arxiv.org/abs/2405.17337
Entity alignment (EA) aims to merge two knowledge graphs (KGs) by identifying equivalent entity pairs. While existing methods heavily rely on human-generated labels, it is prohibitively expensive to incorporate cross-domain experts for annotation in
Externí odkaz:
http://arxiv.org/abs/2405.16806
Knowledge-based visual question answering (KVQA) has been extensively studied to answer visual questions with external knowledge, e.g., knowledge graphs (KGs). While several attempts have been proposed to leverage large language models (LLMs) as an i
Externí odkaz:
http://arxiv.org/abs/2402.12728
Generating accurate Structured Querying Language (SQL) is a long-standing problem, especially in matching users' semantic queries with structured databases and then generating structured SQL. Existing models typically input queries and database schem
Externí odkaz:
http://arxiv.org/abs/2402.13284
Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-world applications. Nonetheless, LLMs are often criticized for their tendency to produce hallucinations, wherein the models fabricate incorrect statements on tasks be
Externí odkaz:
http://arxiv.org/abs/2312.06185