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Mingjie Li

13 papers hereh-index 7139 citations15 works total

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

author position
  • middle author11
  • last author1

Across the 12 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CR4
  • cs.LG4
  • cs.CV3
  • cs.CL2
same name
  • Mingjie Li — 13 papers, h 12
  • Mingjie Li — 12 papers, h 9
  • Mingjie Li — 9 papers, h 4
  • Mingjie Li — 7 papers, h 9
  • Mingjie Li — 5 papers, h 2
  • Mingjie Li — 5 papers, h 2

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
20242026
most citedGenerated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data

1 citations · 1 across the 10 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

GEO-Flag: Detecting and Measuring GEO-Optimized Web Content

Junjie Chu, Ye Leng, Mingjie Li +3

Generative Engine Optimization (GEO) modifies web content to increase its likelihood of being selected and cited by generative search engines. This can give strategically optimized…

cs.LG2026

Pruning Unsafe Tickets: A Resource-Efficient Framework for Safer and More Robust LLMs

Wai Man Si, Mingjie Li, Michael Backes +1

Machine learning models are increasingly deployed in real-world applications, but even aligned models such as Mistral and LLaVA still exhibit unsafe behaviors inherited from pre-tr…

cs.LG2026

Sparse Models, Sparse Safety: Unsafe Routes in Mixture-of-Experts LLMs

Yukun Jiang, Hai Huang, Mingjie Li +3

By introducing routers to selectively activate experts in Transformer layers, the mixture-of-experts (MoE) architecture significantly reduces computational costs in large language…

cs.LG2025

SaLoRA: Safety-Alignment Preserved Low-Rank Adaptation

Mingjie Li, Wai Man Si, Michael Backes +2

As advancements in large language models (LLMs) continue and the demand for personalized models increases, parameter-efficient fine-tuning (PEFT) methods (e.g., LoRA) will become e…

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