10 papers
UltraData-Code: From Raw Repositories to Algorithmically Dense and Task-Oriented Code Data
Chengying Tu, Hengyu Zhao, Shuaikang Xue +13
Code generation has become a core capability of large language models (LLMs), and code data is central to the pre-training process that develops it. As obtaining public code of suf…
ReaORE: Reasoning-Guided Progressive Open Relation Extraction Empowered by Large Reasoning Models
Xin Lin, Liang Zhang, Guoqi Ma +2
Open Relation Extraction (OpenRE) requires a model to extract unseen relations between head and tail entities from unstructured text for real-world applications. The core challenge…
Data Science and Technology Towards AGI Part I: Tiered Data Management
Yudong Wang, Zixuan Fu, Hengyu Zhao +14
The development of artificial intelligence can be viewed as an evolution of data-driven learning paradigms, with successive shifts in data organization and utilization continuously…
ChartM: A Multi-Stage Code-Driven Pipeline for Constructing Multi-Dimensional and Multi-Step Visual Reasoning Data in Chart Comprehension
Duo Xu, Hao Cheng, Xin Lin +2
Complex chart understanding tasks demand advanced visual recognition and reasoning capabilities from multimodal large language models (MLLMs). However, current research provides li…
LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models
Hongyao Tu, Liang Zhang, Yujie Lin +4
The goal of open relation extraction (OpenRE) is to develop an RE model that can generalize to new relations not encountered during training. Existing studies primarily formulate O…
Ask-to-Clarify: Resolving Instruction Ambiguity through Multi-turn Dialogue
Xingyao Lin, Xinghao Zhu, Tianyi Lu +6
Embodied agents are intelligent systems designed to perceive, reason, and act within the physical world. While the robotics community has long strived to build such versatile agent…
Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation
Jinyi Han, Tingyun Li, Shisong Chen +8
While large language models (LLMs) have demonstrated remarkable performance across diverse tasks, they fundamentally lack self-awareness and frequently exhibit overconfidence, assi…
A Stitch in Time Saves Nine: Proactive Self-Refinement for Language Models
Jinyi Han, Xinyi Wang, Haiquan Zhao +9
Recent advances in self-refinement have demonstrated significant potential for improving the outputs of large language models (LLMs) through iterative refinement. However, most exi…
An Automated Multi-modal Evaluation Framework for Mobile Intelligent Assistants Based on Large Language Models and Multi-Agent Collaboration
Meiping Wang, Jian Zhong, Rongduo Han +6
With the rapid development of mobile intelligent assistant technologies, multi-modal AI assistants have become essential interfaces for daily user interactions. However, current ev…
Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience
Haixia Han, Tingyun Li, Shisong Chen +5
Large Language Models (LLMs) have exhibited remarkable performance across various downstream tasks, but they may generate inaccurate or false information with a confident tone. One…