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Zhi Zhou

4 papers here

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

author position
  • first author2
  • middle author2

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

fields
  • cs.AI2
  • cs.CL1
  • cs.CV1
same name
  • Zhi Zhou — 23 papers, h 29
  • Zhi Zhou — 18 papers
  • Zhi Zhou — 17 papers, h 38
  • Zhi Zhou — 7 papers, h 2
  • Zhi Zhou — 7 papers, h 0
  • Zhi Zhou — 7 papers, h 0

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

most citedLawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model

16 citations · 16 across the 1 of their papers we have counts for

collaborators

4 papers

cs.AI2024

Enabling Small Models for Zero-Shot Selection and Reuse through Model Label Learning

Jia Zhang, Zhi Zhou, Lan-Zhe Guo +1

Vision-language models (VLMs) like CLIP have demonstrated impressive zero-shot ability in image classification tasks by aligning text and images but suffer inferior performance com…

cs.CL2024★ 16 cited

LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model

Zhi Zhou, Jiang-Xin Shi, Peng-Xiao Song +4

Large language models (LLMs), including both proprietary and open-source models, have showcased remarkable capabilities in addressing a wide range of downstream tasks. Nonetheless,…

cs.AI2024

VCSearch: Bridging the Gap Between Well-Defined and Ill-Defined Problems in Mathematical Reasoning

Shi-Yu Tian, Zhi Zhou, Kun-Yang Yu +4

Large language models (LLMs) have demonstrated impressive performance on reasoning tasks, including mathematical reasoning. However, the current evaluation mostly focuses on carefu…

cs.CV2024

DeCoOp: Robust Prompt Tuning with Out-of-Distribution Detection

Zhi Zhou, Ming Yang, Jiang-Xin Shi +2

Vision-language models (VLMs), such as CLIP, have demonstrated impressive zero-shot capabilities for various downstream tasks. Their performance can be further enhanced through few…

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