4 citations · 14 across the 9 of their papers we have counts for
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cs.CL2026
Towards a Learnable Retriever for Textual Graph Question Answering
Kaifeng Jin, Yuchen Yan, Zhichen Zeng +4
Textual graph question answering (GraphQA) and graph-based retrieval-augmented generation (GraphRAG) have gained increasing attention as a way to ground large language models (LLMs…
cs.CL2024★ 1 cited
Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answering
Yao Xu, Shizhu He, Jiabei Chen +6
To address the issues of insufficient knowledge and hallucination in Large Language Models (LLMs), numerous studies have explored integrating LLMs with Knowledge Graphs (KGs). Howe…
cs.CL2024★ 4 cited
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?
Qineng Wang, Zihao Wang, Ying Su +2
Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experime…