41 citations · 195 across the 42 of their papers we have counts for
43 papers
CrossModalQA: A Cross-modal and Multi-hop Benchmark for Multimodal Retrieval-augmented Generation
Jiacheng Cai, Zijin Hong, Zheng Yuan +3
Despite the strong capabilities of multimodal large language models (MLLMs), their parametric knowledge remains incomplete and difficult to update, motivating multimodal retrieval-…
Evaluating LLMs on Conversational Text-to-SQL under Chain Ambiguity and Intent Drift
Yujia Liu, Jiayan Lin, Zijin Hong +6
Recent advances in large language models (LLMs) have established conversational text-to-SQL as a practical interface between users and databases, often involving multiple turns of…
Are These Modules Worth Their Cost? A Paradigm-Level Accuracy-Cost Analysis of In-context Learning Text-to-SQL
Jiayan Lin, Yujia Liu, Zijin Hong +6
Recent advances in in-context learning (ICL) text-to-SQL have substantially improved execution accuracy on public benchmarks by assembling increasingly elaborate pipelines around t…
Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence
Yuyuan Feng, Zhishang Xiang, Chaobin Yang +32
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit…
Slides2MindMap: Reconstructing Cognitively Efficient Knowledge Hierarchies from Lecture Slides
Yuzhi Wang, Rongjun Ye, Shengyuan Chen +5
Generating mind maps from lecture slides can help learners efficiently assimilate fragmented knowledge, promising substantial benefits for intelligent education. However, dedicated…
Zero-Mem: Zero-Token Memory Operations for LLM Agents
Yilin Xiao, Zhehan Zhu, Yujing Zhang +8
LLM agents need memory to act consistently over long interactions, yet many systems use additional LLM calls to operate that memory. Generating intermediate records and mediating t…
MoG: Mixture of Experts for Graph-based Retrieval-Augmented Generation
Zheng Yuan, Chuang Zhou, Linhao Luo +4
Retrieval-augmented generation is intensively studied to ground large language models on external evidence. However, retrieving from a unified knowledge base could inevitably intro…
LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning
Zerui Chen, Qinggang Zhang, Zhishang Xiang +5
Graph-based Retrieval-Augmented Generation (GraphRAG) advances flat document retrieval by structuring knowledge as relational graphs, enabling more coherent and effective reasoning…
Toward Native Multimodal Modeling: A Roadmap
Siyu An, Junru Lu, Junnan Dong +18
Multimodal modeling represents a vital step from modality-agnostic reasoning toward world modeling. While early approaches predominantly rely on late-fusion that assembles encoders…
LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation
Yilin Xiao, Jin Chen, Qinggang Zhang +6
Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) by grounding their responses in structured knowledge graph…
ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement
Zijin Hong, Hao Chen, Zheng Yuan +6
Despite the remarkable performance of large language models (LLMs) in text-to-SQL (SQL generation), correctly producing SQL queries remains challenging during initial generation. T…
Graph-based Agent Memory: Taxonomy, Techniques, and Applications
Chang Yang, Chuang Zhou, Yilin Xiao +15
Memory emerges as the core module in the Large Language Model (LLM)-based agents for long-horizon complex tasks (e.g., multi-turn dialogue, game playing, scientific discovery), whe…
Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs
Su Dong, Qinggang Zhang, Yilin Xiao +3
Large language models (LLMs) often struggle with knowledge-intensive tasks due to hallucinations and outdated parametric knowledge. While Retrieval-Augmented Generation (RAG) addre…
LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora
Luyao Zhuang, Shengyuan Chen, Yilin Xiao +5
Retrieval-Augmented Generation (RAG) is widely used to mitigate hallucinations of Large Language Models (LLMs) by leveraging external knowledge. While effective for simple queries,…
Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning
Junnan Dong, Siyu An, Yifei Yu +6
Graph retrieval-augmented generation (GraphRAG) has effectively enhanced large language models in complex reasoning by organizing fragmented knowledge into explicitly structured gr…
LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrieval
Luyao Zhuang, Qinggang Zhang, Huachi Zhou +2
Tool learning has emerged as a promising paradigm for large language models (LLMs) to solve many real-world tasks. Nonetheless, with the tool repository rapidly expanding, it is im…
You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures
Shengyuan Chen, Chuang Zhou, Zheng Yuan +6
Large language models (LLMs) often suffer from hallucination, generating factually incorrect statements when handling questions beyond their knowledge and perception. Retrieval-aug…
LAG: Logic-Augmented Generation from a Cartesian Perspective
Yilin Xiao, Chuang Zhou, Yujing Zhang +5
Large language models (LLMs) have demonstrated remarkable capabilities across a wide range of tasks, yet exhibit critical limitations in knowledge-intensive tasks, often generating…
GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generation
Yilin Xiao, Junnan Dong, Chuang Zhou +5
Graph Retrieval Augmented Generation (GraphRAG) has garnered increasing recognition for its potential to enhance large language models (LLMs) by structurally organizing domain-spec…
Reliable Reasoning Path: Distilling Effective Guidance for LLM Reasoning with Knowledge Graphs
Yilin Xiao, Chuang Zhou, Qinggang Zhang +3
Large language models (LLMs) often struggle with knowledge-intensive tasks due to a lack of background knowledge and a tendency to hallucinate. To address these limitations, integr…
When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation
Zhishang Xiang, Chuanjie Wu, Qinggang Zhang +4
Graph retrieval-augmented generation (GraphRAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) with external knowledge. It leverages graphs to model…
AliBoost: Ecological Boosting Framework in Alibaba Platform
Qijie Shen, Yuanchen Bei, Zihong Huang +8
Maintaining a healthy ecosystem in billion-scale online platforms is challenging, as users naturally gravitate toward popular items, leaving cold and less-explored items behind. Th…
Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation
Zheng Yuan, Hao Chen, Zijin Hong +4
Generating SQLs from user queries is a long-standing challenge, where the accuracy of initial schema linking significantly impacts subsequent SQL generation performance. However, c…
Automated Heterogeneous Network learning with Non-Recursive Message Passing
Zhaoqing Li, Maiqi Jiang, Shengyuan Chen +3
Heterogeneous information networks (HINs) can be used to model various real-world systems. As HINs consist of multiple types of nodes, edges, and node features, it is nontrivial to…