1 citations · 2 across the 11 of their papers we have counts for
12 papers
Towards Reliable AI Data Scientists: Data Agents with Workflow Harnesses
Huachi Zhou, Yujing Zhang, Jiahe Du +7
Large language model agents are increasingly deployed for data-intensive work, yet reliable data analysis requires more than general-purpose reasoning and ad hoc tool augmentation.…
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…
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…
Deep Tabular Research via Continual Experience-Driven Execution
Junnan Dong, Chuang Zhou, Zheng Yuan +7
Large language models often struggle with complex long-horizon analytical tasks over unstructured tables, which typically feature hierarchical and bidirectional headers and non-can…
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Large language models (LLMs) often struggle with knowledge-intensive tasks due to hallucinations and outdated parametric knowledge.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…
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…
Each Graph is a New Language: Graph Learning with LLMs
Huachi Zhou, Jiahe Du, Chuang Zhou +4
Recent efforts leverage Large Language Models (LLMs) for modeling text-attributed graph structures in node classification tasks. These approaches describe graph structures for LLMs…
Cost-efficient Knowledge-based Question Answering with Large Language Models
Junnan Dong, Qinggang Zhang, Chuang Zhou +3
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…