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Bryan Hooi

15 papers hereh-index 9319 citations16 works total

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

author position
  • middle author8
  • last author7

Across the 15 of 15 papers where every author was matched, so the position is known.

fields
  • cs.CL6
  • cs.CR5
  • cs.AI3
  • cs.SI1
same name
  • Bryan Hooi — 43 papers, h 17
  • Bryan Hooi — 37 papers, h 17
  • Bryan Hooi — 26 papers, h 13
  • Bryan Hooi — 22 papers, h 29
  • Bryan Hooi — 18 papers, h 9
  • Bryan Hooi — 16 papers, h 10

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
20222025
most citedKnowPhish: Large Language Models Meet Multimodal Knowledge Graphs for Enhancing Reference-Based Phishing Detection

8 citations · 20 across the 15 of their papers we have counts for

collaborators
Showing 2025 · cs.CLShow all

4 papers · 2 filters

cs.CL2025

Automating Steering for Safe Multimodal Large Language Models

Lyucheng Wu, Mengru Wang, Ziwen Xu +4

Recent progress in Multimodal Large Language Models (MLLMs) has unlocked powerful cross-modal reasoning abilities, but also raised new safety concerns, particularly when faced with…

cs.CL2025

ChineseHarm-Bench: A Chinese Harmful Content Detection Benchmark

Kangwei Liu, Siyuan Cheng, Bozhong Tian +7

Large language models (LLMs) have been increasingly applied to automated harmful content detection tasks, assisting moderators in identifying policy violations and improving the ov…

cs.CL2025

LookAhead Tuning: Safer Language Models via Partial Answer Previews

Kangwei Liu, Mengru Wang, Yujie Luo +7

Fine-tuning enables large language models (LLMs) to adapt to specific domains, but often compromises their previously established safety alignment. To mitigate the degradation of m…

cs.CL2025

ReLearn: Unlearning via Learning for Large Language Models

Haoming Xu, Ningyuan Zhao, Liming Yang +7

Current unlearning methods for large language models usually rely on reverse optimization to reduce target token probabilities. However, this paradigm disrupts the subsequent token…

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