57 citations · 122 across the 8 of their papers we have counts for
8 papers
HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing
Jing Chen, Xinyu Zhu, Cheng Yang +8
Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in natural language processing. In particular, la…
ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models
Yuxiang Zhang, Jing Chen, Junjie Wang +10
Tool-augmented large language models (LLMs) are rapidly being integrated into real-world applications. Due to the lack of benchmarks, the community has yet to fully understand the…
Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation
Jiashu Pu, Yajing Wan, Yuru Zhang +6
Previous in-context learning (ICL) research has focused on tasks such as classification, machine translation, text2table, etc., while studies on whether ICL can improve human-like…
Agents: An Open-source Framework for Autonomous Language Agents
Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li +15
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with e…
LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities
Yuqi Zhu, Xiaohan Wang, Jing Chen +6
This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We engage in experime…
Revisiting k-NN for Fine-tuning Pre-trained Language Models
Lei Li, Jing Chen, Bozhong Tian +1
Pre-trained Language Models (PLMs), as parametric-based eager learners, have become the de-facto choice for current paradigms of Natural Language Processing (NLP). In contrast, k-N…
CodeKGC: Code Language Model for Generative Knowledge Graph Construction
Zhen Bi, Jing Chen, Yinuo Jiang +4
Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specificati…
Relphormer: Relational Graph Transformer for Knowledge Graph Representations
Zhen Bi, Siyuan Cheng, Jing Chen +3
Transformers have achieved remarkable performance in widespread fields, including natural language processing, computer vision and graph mining. However, vanilla Transformer archit…