9 papers · 1 filter
CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph
Chengtao Gan, Xiaoke Guo, Yushan Zhu +5
The continuous evolution of large language models drives escalating demands on data scale and quality, and as different training stages impose increasingly tailored data requiremen…
Symbolic and Abstractive Reasoning with Complex Visual Queries
Yichi Zhang, Jingdian Lu, Zhuo Chen +4
Understanding and reasoning over abstract visual content remains a challenge for current multi-modal large language models (MLLMs). In this paper, we explore a novel abstract data…
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…
ASTRA: Adaptive Semantic Tree Reasoning Architecture for Complex Table Question Answering
Xiaoke Guo, Songze Li, Zhiqiang Liu +4
Table serialization remains a critical bottleneck for Large Language Models (LLMs) in complex table question answering, hindered by challenges such as structural neglect, represent…
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…
CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs
Yuanxiang Liu, Songze Li, Xiaoke Guo +4
Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities but often grapple with reliability challenges like hallucinations. While Knowledge Graphs (KGs) off…
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…
Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching
Songze Li, Zhiqiang Liu, Zhengke Gui +2
Large Language Models (LLMs) exhibit strong reasoning capabilities in complex tasks. However, they still struggle with hallucinations and factual errors in knowledge-intensive scen…
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…