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Jie Tian

4 papers hereh-index 565 citations5 works total

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

author position
  • first author1
  • middle author2
  • last author1

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

fields
  • cs.CL3
  • cs.CV1
same name
  • Jie Tian — 5 papers, h 3
  • Jie Tian — 3 papers, h 4
  • Jie Tian — 3 papers, h 4
  • Jie Tian — 3 papers, h 2
  • Jie Tian — 2 papers, h 3
  • Jie Tian — 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

most citedMitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models

2 citations · 2 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CL2025

Chinese-Vicuna: A Chinese Instruction-following Llama-based Model

Chenghao Fan, Zhenyi Lu, Jie Tian

Chinese-Vicuna is an open-source, resource-efficient language model designed to bridge the gap in Chinese instruction-following capabilities by fine-tuning Meta's LLaMA architectur…

cs.CV2025

Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You Think

Jie Tian, Xiaoye Qu, Zhenyi Lu +3

Image-to-Video (I2V) generation aims to synthesize a video clip according to a given image and condition (e.g., text). The key challenge of this task lies in simultaneously generat…

cs.CL2024★ 2 cited

Mitigating Boundary Ambiguity and Inherent Bias for Text Classification in the Era of Large Language Models

Zhenyi Lu, Jie Tian, Wei Wei +4

Text classification is a crucial task encountered frequently in practical scenarios, yet it is still under-explored in the era of large language models (LLMs). This study shows tha…

cs.CL2024

On Giant's Shoulders: Effortless Weak to Strong by Dynamic Logits Fusion

Chenghao Fan, Zhenyi Lu, Wei Wei +4

Efficient fine-tuning of large language models for task-specific applications is imperative, yet the vast number of parameters in these models makes their training increasingly cha…

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