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researcher

Sheng Shen

University of California, Berkeley

4 papers hereh-index 3428k citations50 works total

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

author position
  • middle author1

Across the 1 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI2
  • cs.CV2
affiliations
  • University of California, Berkeley
Homepage
same name
  • Sheng Shen — 7 papers, h 6
  • Sheng Shen — 6 papers, h 2
  • Sheng Shen — 5 papers, h 6
  • Sheng Shen — 3 papers, h 3
  • Sheng Shen — 2 papers, h 3
  • Sheng Shen — 2 papers, h 9

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

collaborators

4 papers

cs.AI2025

AgentBench: Evaluating LLMs as Agents

Xiao Liu, Hao Yu, Hanchen Zhang +19

The potential of Large Language Model (LLM) as agents has been widely acknowledged recently. Thus, there is an urgent need to quantitatively \textit{evaluate LLMs as agents} on cha…

cs.CV2024

Enhancing Large Vision Language Models with Self-Training on Image Comprehension

Yihe Deng, Pan Lu, Fan Yin +6

Large vision language models (LVLMs) integrate large language models (LLMs) with pre-trained vision encoders, thereby activating the perception capability of the model to understan…

cs.AI2024

The Llama 3 Herd of Models

Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…

cs.CV2024

MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens

Anas Awadalla, Le Xue, Oscar Lo +11

Multimodal interleaved datasets featuring free-form interleaved sequences of images and text are crucial for training frontier large multimodal models (LMMs). Despite the rapid pro…

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