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Zhongyuan Wang

10 papers hereh-index 81.1k citations16 works total

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

author position
  • middle author7
  • last author2

Across the 9 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.RO3
  • cs.CV2
  • eess.SY1
same name
  • Zhongyuan Wang — 31 papers, h 16
  • Zhongyuan Wang — 25 papers, h 32
  • Zhongyuan Wang — 16 papers, h 48
  • Zhongyuan Wang — 15 papers, h 11
  • Zhongyuan Wang — 11 papers, h 5
  • Zhongyuan Wang — 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
20232026
most citedEmu3: Next-Token Prediction is All You Need

4 citations · 10 across the 10 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

Knowledge Reasoning Language Model: Unifying Knowledge and Language for Inductive Knowledge Graph Reasoning

Xingrui Zhuo, Jiapu Wang, Gongqing Wu +4

Inductive Knowledge Graph Reasoning (KGR) aims to discover facts in open-domain KGs containing unknown entities and relations, which poses a challenge for KGR models in comprehendi…

cs.CL2024★ 1 cited

52B to 1T: Lessons Learned via Tele-FLM Series

Xiang Li, Yiqun Yao, Xin Jiang +17

Large Language Models (LLMs) represent a significant stride toward Artificial General Intelligence. As scaling laws underscore the potential of increasing model sizes, the academic…

cs.CL2024

Tele-FLM Technical Report

Xiang Li, Yiqun Yao, Xin Jiang +17

Large language models (LLMs) have showcased profound capabilities in language understanding and generation, facilitating a wide array of applications. However, there is a notable p…

cs.CL2024★ 1 cited

Not All Layers of LLMs Are Necessary During Inference

Siqi Fan, Xin Jiang, Xiang Li +6

Due to the large number of parameters, the inference phase of Large Language Models (LLMs) is resource-intensive. However, not all requests posed to LLMs are equally difficult to h…

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