1 citations · 1 across the 6 of their papers we have counts for
10 papers
GraphGhost: Tracing Structures Behind Large Language Models
Xinnan Dai, Xianxuan Long, Chung-Hsiang Lo +4
Large Language Models (LLMs) exhibit strong reasoning capabilities on structured tasks, yet the internal mechanisms underlying such behaviors remain poorly understood. Existing int…
Beyond Static Retrieval: Opportunities and Pitfalls of Iterative Retrieval in GraphRAG
Kai Guo, Xinnan Dai, Shenglai Zeng +4
Retrieval-augmented generation (RAG) is a powerful paradigm for improving large language models (LLMs) on knowledge-intensive question answering. Graph-based RAG (GraphRAG) leverag…
Uncovering Graph Reasoning in Decoder-only Transformers with Circuit Tracing
Xinnan Dai, Chung-Hsiang Lo, Kai Guo +3
Transformer-based LLMs demonstrate strong performance on graph reasoning tasks, yet their internal mechanisms remain underexplored. To uncover these reasoning process mechanisms in…
Keeping an Eye on LLM Unlearning: The Hidden Risk and Remedy
Jie Ren, Zhenwei Dai, Xianfeng Tang +9
Although Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, growing concerns have emerged over the misuse of sensitive, copyrighte…
Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation
Jiankun Zhang, Shenglai Zeng, Jie Ren +4
Multimodal Retrieval-Augmented Generation (MRAG) systems enhance LMMs by integrating external multimodal databases, but introduce unexplored privacy vulnerabilities. While text-bas…
Empowering GraphRAG with Knowledge Filtering and Integration
Kai Guo, Harry Shomer, Shenglai Zeng +3
In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph…