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Chong Wang

10 papers hereh-index 7463 citations14 works total

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

author position
  • middle author6
  • last author1

Across the 7 of 10 papers where every author was matched, so the position is known.

fields
  • cs.CL5
  • cs.LG3
  • cs.AI1
  • cs.CV1
same name
  • Chong Wang — 93 papers, h 41
  • Chong Wang — 22 papers, h 17
  • Chong Wang — 19 papers, h 14
  • Chong Wang — 16 papers, h 25
  • Chong Wang — 15 papers, h 16
  • Chong Wang — 14 papers, h 14

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 citedMM1: Methods, Analysis & Insights from Multimodal LLM Pre-training

11 citations · 15 across the 10 of their papers we have counts for

collaborators
Showing 2025Show all

4 papers · 1 filter

cs.CL2025

Synthetic bootstrapped pretraining

Zitong Yang, Aonan Zhang, Hong Liu +4

We introduce Synthetic Bootstrapped Pretraining (SBP), a language model (LM) pretraining procedure that first learns a model of relations between documents from the pretraining dat…

cs.LG2025

Apple Intelligence Foundation Language Models: Tech Report 2025

Ethan Li, Anders Boesen Lindbo Larsen, Chen Zhang +395

We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model opti…

cs.CL2025

CommVQ: Commutative Vector Quantization for KV Cache Compression

Junyan Li, Yang Zhang, Muhammad Yusuf Hassan +8

Large Language Models (LLMs) are increasingly used in applications requiring long context lengths, but the key-value (KV) cache often becomes a memory bottleneck on GPUs as context…

cs.CL2025

RATTENTION: Towards the Minimal Sliding Window Size in Local-Global Attention Models

Bailin Wang, Chang Lan, Chong Wang +1

Local-global attention models have recently emerged as compelling alternatives to standard Transformers, promising improvements in both training and inference efficiency. However,…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.