11 papers
IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrieval
Xiaoman Wang, Yaoze Zhang, Wenzhuo Fan +7
Retrieval-Augmented Generation (RAG) has shown strong effectiveness in grounding Large Language Models (LLMs) with external knowledge. However, existing RAG and Graph RAG framework…
Investigating Redundancy in Multimodal Large Language Models with Multiple Vision Encoders
Yizhou Wang, Song Mao, Yang Chen +8
Recent multimodal large language models (MLLMs) increasingly integrate multiple vision encoders to improve performance on various benchmarks, assuming that diverse pretraining obje…
Aligning Vision to Language: Annotation-Free Multimodal Knowledge Graph Construction for Enhanced LLMs Reasoning
Junming Liu, Siyuan Meng, Yanting Gao +7
Multimodal reasoning in Large Language Models (LLMs) struggles with incomplete knowledge and hallucination artifacts, challenges that textual Knowledge Graphs (KGs) only partially…
IWR-Bench: Can LVLMs reconstruct interactive webpage from a user interaction video?
Yang Chen, Minghao Liu, Yufan Shen +18
The webpage-to-code task requires models to understand visual representations of webpages and generate corresponding code. However, existing benchmarks primarily focus on static sc…
LeanRAG: Knowledge-Graph-Based Generation with Semantic Aggregation and Hierarchical Retrieval
Yaoze Zhang, Rong Wu, Pinlong Cai +5
Retrieval-Augmented Generation (RAG) plays a crucial role in grounding Large Language Models by leveraging external knowledge, whereas the effectiveness is often compromised by the…
HetaRAG: Hybrid Deep Retrieval-Augmented Generation across Heterogeneous Data Stores
Guohang Yan, Yue Zhang, Pinlong Cai +7
Retrieval-augmented generation (RAG) has become a dominant paradigm for mitigating knowledge hallucination and staleness in large language models (LLMs) while preserving data secur…