7 citations · 8 across the 18 of their papers we have counts for
19 papers
Scaling LLM Knowledge Boundaries via Distribution-Optimized Synthesis
Songze Li, Yarong Lan, Zhongpu Bo +16
Knowledge injection via synthetic data is crucial for enhancing Large Language Models (LLMs). However, current synthesis methods simply stop at preset token counts or fixed data ra…
CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning
Chengtao Gan, Zhiqiang Liu, Long Jin +3
Real-world scenarios involve massive heterogeneous structured data (e.g., tables, knowledge graphs), making effective reasoning over such diverse data increasingly important. Unifi…
SkillAdaptor: Self-Adapting Skills for LLM Agents from Trajectories
Zhuoyun Yu, Xin Xie, Wuguannan Yao +4
Large language model (LLM) agents increasingly rely on reusable external skills to solve long-horizon interactive tasks. Existing training-free skill adaptation pipelines usually u…
SkillNet: Create, Evaluate, and Connect AI Skills
Yuan Liang, Ruobin Zhong, Haoming Xu +47
Current AI agents can flexibly invoke tools and execute complex tasks, yet their long-term advancement is hindered by the lack of systematic accumulation and transfer of skills. Wi…
Temp-R1: A Unified Autonomous Agent for Complex Temporal KGQA via Reverse Curriculum Reinforcement Learning
Zhaoyan Gong, Zhiqiang Liu, Songze Li +7
Temporal Knowledge Graph Question Answering (TKGQA) is inherently challenging, as it requires sophisticated reasoning over dynamic facts with multi-hop dependencies and complex tem…
Self-Correction Distillation for Structured Data Question Answering
Yushan Zhu, Wen Zhang, Long Jin +8
Structured data question answering (QA), including table QA, Knowledge Graph (KG) QA, and temporal KG QA, is a pivotal research area. Advances in large language models (LLMs) have…
Last Layer Logits to Logic: Empowering LLMs with Logic-Consistent Structured Knowledge Reasoning
Songze Li, Zhiqiang Liu, Zhaoyan Gong +6
Large Language Models (LLMs) achieve excellent performance in natural language reasoning tasks through pre-training on vast unstructured text, enabling them to understand the logic…
Evontree: Ontology Rule-Guided Self-Evolution of Large Language Models
Mingchen Tu, Zhiqiang Liu, Juan Li +4
Although Large Language Models (LLMs) perform exceptionally well in general domains, the problem of hallucinations poses significant risks in specialized fields such as healthcare…
Structured and Abstractive Reasoning on Multi-modal Relational Knowledge Images
Yichi Zhang, Zhuo Chen, Lingbing Guo +2
Understanding and reasoning with abstractive information from the visual modality presents significant challenges for current multi-modal large language models (MLLMs). Among the v…
RTQA : Recursive Thinking for Complex Temporal Knowledge Graph Question Answering with Large Language Models
Zhaoyan Gong, Juan Li, Zhiqiang Liu +3
Current temporal knowledge graph question answering (TKGQA) methods primarily focus on implicit temporal constraints, lacking the capability of handling more complex temporal queri…
Collaboration of Fusion and Independence: Hypercomplex-driven Robust Multi-Modal Knowledge Graph Completion
Zhiqiang Liu, Yichi Zhang, Mengshu Sun +2
Multi-modal knowledge graph completion (MMKGC) aims to discover missing facts in multi-modal knowledge graphs (MMKGs) by leveraging both structural relationships and diverse modali…
SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs
Zhiqiang Liu, Enpei Niu, Yin Hua +4
Although large language models (LLMs) have made significant progress in understanding Structured Knowledge (SK) like KG and Table, existing evaluations for SK understanding are non…
RiOT: Efficient Prompt Refinement with Residual Optimization Tree
Chenyi Zhou, Zhengyan Shi, Yuan Yao +3
Recent advancements in large language models (LLMs) have highlighted their potential across a variety of tasks, but their performance still heavily relies on the design of effectiv…
UniHR: Hierarchical Representation Learning for Unified Knowledge Graph Link Prediction
Zhiqiang Liu, Yin Hua, Mingyang Chen +4
Real-world knowledge graphs (KGs) contain not only standard triple-based facts, but also more complex, heterogeneous types of facts, such as hyper-relational facts with auxiliary k…
Croppable Knowledge Graph Embedding
Yushan Zhu, Wen Zhang, Zhiqiang Liu +3
Knowledge Graph Embedding (KGE) is a common approach for Knowledge Graphs (KGs) in AI tasks. Embedding dimensions depend on application scenarios. Requiring a new dimension means t…
TrustUQA: A Trustful Framework for Unified Structured Data Question Answering
Wen Zhang, Long Jin, Yushan Zhu +6
Natural language question answering (QA) over structured data sources such as tables and knowledge graphs have been widely investigated, especially with Large Language Models (LLMs…
InBox: Recommendation with Knowledge Graph using Interest Box Embedding
Zezhong Xu, Yincen Qu, Wen Zhang +2
Knowledge graphs (KGs) have become vitally important in modern recommender systems, effectively improving performance and interpretability. Fundamentally, recommender systems aim t…
Prompt-fused framework for Inductive Logical Query Answering
Zezhong Xu, Peng Ye, Lei Liang +2
Answering logical queries on knowledge graphs (KG) poses a significant challenge for machine reasoning. The primary obstacle in this task stems from the inherent incompleteness of…
Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion
Yichi Zhang, Zhuo Chen, Lei Liang +2
Multi-modal knowledge graph completion (MMKGC) aims to predict the missing triples in the multi-modal knowledge graphs by incorporating structural, visual, and textual information…