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Jing Chen

8 papers hereh-index 6618 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author6

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CL8
same name
  • Jing Chen — 53 papers, h 131
  • Jing Chen — 13 papers, h 6
  • Jing Chen — 11 papers, h 33
  • Jing Chen — 9 papers, h 14
  • Jing Chen — 8 papers, h 44
  • Jing Chen — 8 papers, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20222024
most citedRelphormer: Relational Graph Transformer for Knowledge Graph Representations

57 citations · 122 across the 8 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CL2023★ 19 cited

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…

cs.CL2023★ 37 cited

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…

cs.CL2023

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…

cs.CL2023★ 8 cited

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…

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