10 citations · 28 across the 12 of their papers we have counts for
12 papers
Rethinking Memory as Continuously Evolving Connectivity
Jizhan Fang, Buqiang Xu, Zhixian Wang +12
Existing memory-augmented LLM agents often treat memory as a static repository with pre-defined representations and fixed retrieval pipelines, which is brittle in dynamic agentic e…
Chat2Workflow: A Benchmark for Generating Executable Visual Workflows with Natural Language
Yi Zhong, Buqiang Xu, Yijun Wang +4
At present, executable visual workflows have emerged as a mainstream paradigm in real-world industrial deployments, offering strong reliability and controllability. However, in cur…
OceanPile: A Large-Scale Multimodal Ocean Corpus for Foundation Models
Yida Xue, Ningyu Zhang, Tingwei Wu +5
The vast and underexplored ocean plays a critical role in regulating global climate and supporting marine biodiversity, yet artificial intelligence has so far delivered limited imp…
SkillX: Automatically Constructing Skill Knowledge Bases for Agents
Chenxi Wang, Zhuoyun Yu, Xin Xie +8
Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing self-evolving paradigms remain inefficient: agents learn in isolation, r…
How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities
Ziwen Xu, Kewei Xu, Haoming Xu +8
Large Language Models (LLMs) are increasingly deployed in socially sensitive domains, yet their unpredictable behaviors, ranging from misaligned intent to inconsistent personality,…
EasyEdit2: An Easy-to-use Steering Framework for Editing Large Language Models
Ziwen Xu, Shuxun Wang, Kewei Xu +7
In this paper, we introduce EasyEdit2, a framework designed to enable plug-and-play adjustability for controlling Large Language Model (LLM) behaviors. EasyEdit2 supports a wide ra…
Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition
Kehua Feng, Keyan Ding, Hongzhi Tan +8
Reliable evaluation of large language models (LLMs) is impeded by two key challenges: objective metrics often fail to reflect human perception of natural language, and exhaustive h…
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models
Yixin Ou, Ningyu Zhang, Honghao Gui +9
In recent years, instruction tuning has gained increasing attention and emerged as a crucial technique to enhance the capabilities of Large Language Models (LLMs). To construct hig…
OceanGPT: A Large Language Model for Ocean Science Tasks
Zhen Bi, Ningyu Zhang, Yida Xue +4
Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recentl…
When Do Program-of-Thoughts Work for Reasoning?
Zhen Bi, Ningyu Zhang, Yinuo Jiang +3
In the realm of embodied artificial intelligence, the reasoning capabilities of Large Language Models (LLMs) play a pivotal role. Although there are effective methods like program-…
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models
Peng Wang, Ningyu Zhang, Bozhong Tian +11
Large Language Models (LLMs) usually suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to ou…
DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population
Ningyu Zhang, Xin Xu, Liankuan Tao +19
We present an open-source and extensible knowledge extraction toolkit DeepKE, supporting complicated low-resource, document-level and multimodal scenarios in the knowledge base pop…