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researcher

Chunting Zhou

Carnegie Mellon University

31 papers hereh-index 276.8k citations39 works total

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

author position
  • first author12
  • middle author15
  • last author3

Across the 30 of 31 papers where every author was matched, so the position is known.

fields
  • cs.CL23
  • cs.LG6
  • cs.AI1
  • cs.HC1
affiliations
  • Carnegie Mellon University
Homepage
same name
  • Chunting Zhou — 4 papers, h 5
  • Chunting Zhou — 2 papers, h 2
  • Chunting Zhou — 1 paper

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
20152024
most citedA C-LSTM Neural Network for Text Classification

655 citations · 1.3k across the 31 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.CL2024

Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models

Weixin Liang, Lili Yu, Liang Luo +8

The development of large language models (LLMs) has expanded to multi-modal systems capable of processing text, images, and speech within a unified framework. Training these models…

cs.AI2024★ 6 cited

Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model

Chunting Zhou, Lili Yu, Arun Babu +7

We introduce Transfusion, a recipe for training a multi-modal model over discrete and continuous data. Transfusion combines the language modeling loss function (next token predicti…

cs.LG2024★ 1 cited

Megalodon: Efficient LLM Pretraining and Inference with Unlimited Context Length

Xuezhe Ma, Xiaomeng Yang, Wenhan Xiong +7

The quadratic complexity and weak length extrapolation of Transformers limits their ability to scale to long sequences, and while sub-quadratic solutions like linear attention and…

cs.CL2024★ 1 cited

Instruction-tuned Language Models are Better Knowledge Learners

Zhengbao Jiang, Zhiqing Sun, Weijia Shi +6

In order for large language model (LLM)-based assistants to effectively adapt to evolving information needs, it must be possible to update their factual knowledge through continued…

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