JAGDIP SINGH — accepted AI-conference papers

2026

HugRAG: Hierarchical Causal Knowledge Graph Design for RAG

Nengbo Wang, Tuo Liang, Vikash Singh, Chaoda Song, Van Yang, Yu Yin +3

ICML 2026poster

Retrieval augmented generation (RAG) has enhanced large language models by enabling access to external knowledge, with graph-based RAG emerging as a powerful paradigm for structured retrieval and reasoning. However, existing graph-based methods often over-rely on surface-level node matching and lack…

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2025

CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation

Nengbo Wang, Xiaotian Han, Jagdip Singh, Jing Ma, Vipin Chaudhary

ACL 2025finding

Large language models (LLMs) have revolutionized natural language processing (NLP), particularly through Retrieval-Augmented Generation (RAG), which enhances LLM capabilities by integrating external knowledge. However, traditional RAG systems face critical limitations, including disrupted contextual…

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