Papers by Jiabei Chen
Generate-on-Graph: Treat LLM as both Agent and KG for Incomplete Knowledge Graph Question Answering (2024.emnlp-main)
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Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang, Yangqiu Song, Hanghang Tong, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Existing methods to integrate LLMs with Knowledge Graphs (KGs) however, these methods are often incomplete to cover all the knowledge required to answer questions. |
| Approach: | They propose to integrate LLMs with Knowledge Graphs (KGs) to address insufficient knowledge and hallucination issues in Large Language Models. |
| Outcome: | The proposed method outperforms existing methods on two datasets. |
LLaSA: Large Language and Structured Data Assistant (2025.naacl-long)
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Yao Xu, Shizhu He, Jiabei Chen, ZengXiangrong ZengXiangrong, Bingning Wang, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Structured knowledge grounding (SKG) tasks are a key part of many NLP applications. |
| Approach: | They propose a framework for enhancing LLMs' ability to handle structured data . they represent various types of structured data in a unified hypergraph format . |
| Outcome: | The proposed framework outperforms existing methods on SKG tasks using LoRA finetuning. |
Search-in-Context: Efficient Multi-Hop QA over Long Contexts via Monte Carlo Tree Search with Dynamic KV Retrieval (2025.findings-acl)
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| Challenge: | Existing approaches to multihop question answering (MHQA) over long contexts are often neglecting explicit reasoning or incurring expensive computational costs due to full-attention mechanisms over long contextuals. |
| Approach: | They propose a framework that integrates Monte Carlo Tree Search (MCTS) with dynamic key-value retrieval to enable iterative, context-aware reasoning. |
| Outcome: | The proposed framework integrates Monte Carlo Tree Search (MCTS) with dynamic key-value (KV) retrieval to enable iterative, context-aware reasoning. |