14 citations · 31 across the 17 of their papers we have counts for
18 papers
RAdapter: A Routing and Rewriting Adapter for Efficient Hybrid RAG
Yucan Guo, Miao Su, Saiping Guan +6
Retrieval-Augmented Generation (RAG) has become a prevailing paradigm for enhancing Large Language Models (LLMs) with non-parametric knowledge. Vanilla RAG efficiently handles simp…
HiDiffTIR: Hierarchical Difficulty-Aware Policy Optimization for Multi-Turn Tool-Integrated Reasoning
Yucan Guo, Xiaohan Wang, Miao Su +8
Tool-Integrated Reasoning (TIR) is a fundamental capability for LLM agents to solve complex tasks by interacting with external tools iteratively. Reinforcement Learning (RL) has be…
RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning
Yucan Guo, Miao Su, Saiping Guan +4
Retrieval-Augmented Generation (RAG) integrates non-parametric knowledge into Large Language Models (LLMs), typically from unstructured texts and structured graphs. While recent pr…
StruProKGR: A Structural and Probabilistic Framework for Sparse Knowledge Graph Reasoning
Yucan Guo, Saiping Guan, Miao Su +4
Sparse Knowledge Graphs (KGs) are commonly encountered in real-world applications, where knowledge is often incomplete or limited. Sparse KG reasoning, the task of inferring missin…
A Survey of Link Prediction in N-ary Knowledge Graphs
Jiyao Wei, Saiping Guan, Da Li +3
N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fac…
Look Globally and Reason: Two-stage Path Reasoning over Sparse Knowledge Graphs
Saiping Guan, Jiyao Wei, Xiaolong Jin +2
Sparse Knowledge Graphs (KGs), frequently encountered in real-world applications, contain fewer facts in the form of (head entity, relation, tail entity) compared to more populated…
An In-Context Schema Understanding Method for Knowledge Base Question Answering
Yantao Liu, Zixuan Li, Xiaolong Jin +5
The Knowledge Base Question Answering (KBQA) task aims to answer natural language questions based on a given knowledge base. Recently, Large Language Models (LLMs) have shown stron…
Nested Event Extraction upon Pivot Element Recogniton
Weicheng Ren, Zixuan Li, Xiaolong Jin +6
Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. Nested events involve a kind of Pivot Elem…
Semantic Structure Enhanced Event Causality Identification
Zhilei Hu, Zixuan Li, Xiaolong Jin +4
Event Causality Identification (ECI) aims to identify causal relations between events in unstructured texts. This is a very challenging task, because causal relations are usually e…
Few-shot Link Prediction on N-ary Facts
Jiyao Wei, Saiping Guan, Xiaolong Jin +2
Hyper-relational facts, which consist of a primary triple (head entity, relation, tail entity) and auxiliary attribute-value pairs, are widely present in real-world Knowledge Graph…
Rich Event Modeling for Script Event Prediction
Long Bai, Saiping Guan, Zixuan Li +3
Script is a kind of structured knowledge extracted from texts, which contains a sequence of events. Based on such knowledge, script event prediction aims to predict the subsequent…
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning
Zixuan Li, Zhongni Hou, Saiping Guan +7
A Temporal Knowledge Graph (TKG) is a sequence of KGs with respective timestamps, which adopts quadruples in the form of (\emph{subject}, \emph{relation}, \emph{object}, \emph{time…
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning
Zixuan Li, Saiping Guan, Xiaolong Jin +7
A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to different timestamps. TKG reasoning aims to predict potential facts in the future given the historical KG seq…
What is Event Knowledge Graph: A Survey
Saiping Guan, Xueqi Cheng, Long Bai +5
Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are also an essential kind of knowledge in the world, which trigger the spring up of event-centr…
Integrating Deep Event-Level and Script-Level Information for Script Event Prediction
Long Bai, Saiping Guan, Jiafeng Guo +3
Scripts are structured sequences of events together with the participants, which are extracted from the texts.Script event prediction aims to predict the subsequent event given the…
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs
Zixuan Li, Xiaolong Jin, Saiping Guan +4
Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenge…
Link Prediction on N-ary Relational Data Based on Relatedness Evaluation
Saiping Guan, Xiaolong Jin, Jiafeng Guo +2
With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to fill in missing facts for a long time. However, they mainly focu…
Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning
Zixuan Li, Xiaolong Jin, Wei Li +5
Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the future…